In this study, high efficient design tool is developed with several informatics approaches for Multidisciplinary Design Optimization (MDO) and knowledge discovery of supersonic wing design. In this design, Multi-Objective Genetic Algorithm (MOGA) is applied as an optimizer, while Kriging model is also used to reduce computational cost. To obtain the information of the design space, functional ANalysis Of VAriance (ANOVA) and Parallel Coordinate Plot (PCP) is applied. For Kriging model construction, 107 sample points are evaluated. This tool is applied to the multidisciplinary design problem of supersonic wing. The objective functions are to maximize lift to drag ratio and to minimize sonic boom intensity at supersonic cruise, and to minimize wing weight. According to the results, there are trade-off relationships among three objective functions. The ANOVA results indicate that the cambers of the wing section at the root and the kink have an influence on the lift to drag ratio, the inner wing sweep back angle affects the sonic boom intensity, and the camber of wing section at the kink and aspect ratio affect the wing weight. The design space information could be visualized quantitatively from the sampling results with PCP technique. Since the design space exploration using MOGA is carried out based on Kriging surrogate models, the proposed MDO process is effective in terms of computational cost.