Data mining has been performed on the results of an aerodynamic design optimization of a two-stage-to-orbit reusable launch vehicle flyback-booster wing. Three data mining techniques were compared, including self-organizing map, functional analysis of variance, and the rough set theory. The optimization problem had four aerodynamic objective functions and 71 wing shape design variables. The hypothetical design database resulting from the optimization contained a total of 302 solutions which included 102 nondominated solutions. Consequently, the acquired knowledge of the design space consisted of general design characteristics, correlation between objective functions, and the effects of these design variables on the objective functions, for both nondominated as well as all solutions. The comparison also revealed the similarities and differences among the three data mining techniques used in this study. Even though all three techniques discovered detailed design knowledge and the results produced by the combination of all three methods compensated disadvantages of each method when applied individually, it was discovered that the self-organizing map produced the overall best results. Moreover, this study has also shown that the knowledge acquired from both nondominated solutions and from all solutions found was consistent despite the differences between the design spaces. Furthermore, it was shown that data mining is essential for visualizing results of an evolutionary multi-objective optimization problem and extracting useful design knowledge from these results.