These days, the requirements of machines have become more and more complicated, and many of those requirements have to be satisfied simultaneously. In such cases, it would be rational to carry out multi-objective optimization. However, in conventional decision-making problems, we need to give preference based on local information such as trade-off. Although it is difficult to do so, Providing designer with multiple acceptable designs will be of great help for them to give preference, because the designs will serve as a map of the design space. However, to give multiple Pareto solutions, we need to carry out a large number of scalar optimizations, and this is unrealistic. In order to give multiple acceptable designs by a single optimization process, we simulate adaptation strategies of wildlife combined with genetic algorithms from a previous study. We have succeeded in keeping the variation among populations and directions to give multiple acceptable designs, but have failed in controlling of the number of populations. As a follow-up of the previous study, we focus mainly on adaptation of foraging and try to simulate the evolution of species by changing the searching range of design variables. The goals of the proposed method are to find multiple acceptable designs, to keep the variation among populations and to see the changes of searching range of each objective function, to provide designers with a clear map of the design space. In this study, the proposed method isanalyzed using a simple two-variable, two-objective problem whose analytical solution is available.