Multi-Objective Genetic Algorithm (MOGA) is an application of Genetic Algorithm for solving Multi-objective Optimization Problems (MOPs). Many researches on MOGA have been actively reported. Generally, it is difficult to obtain the optimized solution satisfying all objective functions because of their trade-offs. Then, it is necessary to obtain Pareto solutions which are not inferior to other solutions in at least one objective function. In the application of MOGA to engineering design fields, it is not the goal to obtain high-performance Pareto solutions, because it is also important to analyze the obtained Pareto solutions and extract the knowledge in the problem. In order to analyze Pareto solutions obtained by MOGA, it is required to consider both the objective space and the design variable space. In this paper, we define “Non-Correspondence in Linear Relationship” between the objective space and the design variable space. We also try to extract the Non-Correspondence area in Linear Relationship with the index defined in this paper. This paper applies the proposed method to the trajectory designing optimization problem and extracts Non-Correspondence area in Linear Relationship in the acquired Pareto solutions. This paper also applied the index to the method of fitness approximation and showed that the reduction of the number of evaluation can be carried out.