In general, the main purpose of Genetic Algorithm (GA) is to acquire a solution with the highest evaluation value in a single-objective problem or Pareto solutions with various evaluation values in a multi-objective problem. However, in engineering problems, the acquisition of multiple satisfied solutions satisfying certain conditions is often more strongly desired than acquiring a single best solution. In addition, to help set design choices, satisfied solutions should satisfy different design variable patterns from one another. There are multiple objective functions and rather than being maximized/minimized these are intended to approximate certain target values. These multiple objective functions can be unified into a single-objective function by summing up the errors from the target values. Through this unification of objective functions, computing resources for searching can be assigned in terms of the diversity in the design variable space rather than the objective space. Engineering problems often involve numerous constrained optimization problems. In such problems, the unification of objective functions can also be applied to constraints. In this paper, a method for acquiring multiple satisfied solutions by GA in many constrained multiobjective optimization problems is proposed. The proposed method is applied to a real-world problem and compared with some conventional methods to investigate its performance.