A lot of researches on MOGA (Multi-Objective Genetic Algorithm), in which Genetic Algorithm is applied to MOPs (Multi-objective Optimization Problems), have been actively reported. MOGA has been also applied to engineering design fields, then it is important not only to obtain nondominated solutions having high performance but also to analyze the acquired nondominated solutions and extract the knowledge of the problem for the designers. The authors have proposed some analysis methods of acquired solutions by evolutionary computation based on “visualization”. However, these approaches in the analyses aim to analyze them and that is the goal. Designers often need better solutions than the acquired ones or better fitness value on a certain objective function keeping the other fitness values. This paper proposes a search method to user's preference direction based on the reference lines which is originally proposed by Deb et al. In the proposed method, a user selects the preference area in the visualized space plotting the acquired solutions, and reference points are generated in the selected area. Reference lines are defined by connecting between the reference points and the original point. Moreover, a user can move the original point based on his/her desired feature of solutions in the proposed method. This paper carried out three experiments. In the first experiment, we compared the proposed method with NSGA-III and showed that NSGA-III could not search well in the preference area when the optimal direction was quite different from the direction of reference lines and the proposed method could. In the second experiment, we examined the effectiveness of the change of the original point. The result showed that the solutions having the desired features could be acquired by moving the original point. In the third experiment, we examined the effectiveness of focusing one specific objective function by moving original point. The result showed that it was possible to adjust the focus degree of the specific objective function. The second and third experiments were also done in MaOPs, and the results showed that the proposed method could search user's preference directions and change the feature of solutions by moving the original point.