In the design process, it would be much better to give designers more and more acceptable and reasonable design candidates to derive their preferences to meet requirements of multiple high performances. In this paper, we will propose a new method, to give such acceptable design candidates based on genetic algorithms (GAs) with considering something like strategy for adaptation. GAs are search algorithms based on the mechanics of natural selection and natural genetics. Yet, they are no simple random walk but they efficiently exploit historical information to speculate on new search points with expected improved performance. Thus, we expect in GAs to give multiple acceptable and near optimum design candidates just like so many species in natural living things. However, in simple GAs, after specific number of generations, their populations become near one or two specific local optimum solutions. (Hoping for including global optimum solution.) In order to obtain multiple acceptable solutions, we need to perform GAs processes by keeping variations in characters of individuals. The proposed method tries to find multiple acceptable solutions by considering strategy for adaptation into GAs processes, which are food chain, strategy of foraging, death strategy and strategy of reproduction. As a numerical example, we apply the proposed method to simple multi-objective optimization and demonstrate its efficiencies.