Suppose a methodology has what it takes to succeed in terms of its application to real problems. It might then be obvious that the method is a simple, understandable, and usable approach to the problem setting in question. This study elaborates on this highly abstract but obvious and important requirement in the context of multiple objective optimization (MOO) endeavors. We develop an enhanced interactive method to meet the requirement, which crossfertilizes the two most well-known interactive techniques to compensate for the drawbacks of the two methods and capture their positive aspects. Special emphases are also placed on what the current development makes a considerable improvement in comparison to some other relevant methods. The developed method is then applied to a real-world case problem involving several highly nonlinear objective functions, the problem of operational design and production target setting for the opening branches of a fast-food company. We demonstrate in detail the entire process of the application from modeling a nonlinear MOO problem to generating its solution, in order to guide the practical use of our method to the other potential applications. During the last five decades, a great deal of theories and methods have been developed to resolve the MOO problems. Interactive approach is one of the most widely used families of the MOO techniques. This is based upon a human-computer interaction process in that the computer algorithm generates and presents a solution to the human decision maker, and the decision maker then provides the algorithm with information, so this process repeats until the final solution satisfies the decision maker. The show-and-tell approach articulates the decision maker’s preferences gradually and, hence, helps her to provide her preferences in a step-by -step manner, unlike other families of the MOO techniques requiring all such information at a given moment. Furthermore, the decision maker can learn about the changing pattern of the generated solutions and anticipate the next solution during the solution process, which is also of great help in both information supply and desirable solution choice. Many different interactive methods constitute the family, interactive approach, and they each have different computational algorithms and require different types of information from the decision maker. The success of an interactive method usually lies in how easy and comfortable it is in terms of not only its computational aspect but also information requirement. We thus delve deeply into the characteristics of several representative interactive methods and take advantage of their positive features. As a result, a significantly improved interactive method is developed, thus minimizing both computational and informational burdens on the decision maker. This paper also explores an important managerial problem of operational design and production target setting for opening branches in a fast-food company. All the branches utilize multiple inputs, such as manpower and operating costs, to produce multiple outputs like revenue and customer satisfaction. It is therefore needed to plan as to how much inputs should be used and how much outputs should be produced, before the new branch’s operation. The input setting is referred to as operational design and the output setting as production target setting. To accomplish this, we model a nonlinear MOO problem and apply our method to that problem. It is worth mentioning that there are various cases of opening new branches in many different kinds of industries. Examples are fast-food franchise restaurants, telecommunication service offices, and bank branches. Possible areas of the application of our modeling idea and method are therefore numerous.