Sonic boom suppression is a key technology that must be broken through in the development of a new generation of supersonic civil aircraft. The reasonable optimization of configuration parameters can make the aircraft have good sonic boom characteristics. In order to break through the bottleneck of evolutionary algorithm in optimizing large-scale design variables, a hierarchical optimization method based on data mining is proposed, the decesion tree algorithm in data mining is used to extract the design knowledge, obtain the hierarchical information of design variables, and guide the configuration optimization of low sonic boom aircraft. For a low boom supersonic aircraft, five configuration parameters, including sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables, carry out numerical experiments of hierarchical optimization, and compare with the integrated optimization. Because the optimization process of evolutionary algorithm is random, 100 numerical experiments are carried out to obtain statistical results.The results shows that the hierarchical optimization can obtain the optimal solution consistent with the integrated optimization, and the convergence speed of hierarchical optimization is significantly faster than that of integrated optimization, and the performance of different optimization processes is more robust.