In this study, the high-speed impulsive (HSI) noise and aerodynamic performance of helicopter blades were improved using multi-objective design exploration (MODE) comprised of multi-objective shape optimization and data mining. To broaden the design space of the problem, geometry definitions with a high degree of freedom were adopted. As a result, remarkable improvements in both HSI noise and aerodynamic performance were achieved. For data mining, analysis of variance (ANOVA) and self-organizing map (SOM) were used to extract design knowledge of the helicopter blades. The results indicated that tip chord length and blade twist are important factors for HSI noise and aerodynamic performance, respectively. Based on the design knowledge obtained from data mining, an additional design variable (twist) was introduced. The solutions obtained from the shape optimization using variable twist showed better blade loading performance than that from shape optimization using fixed twist.