A multimodal parameter optimization method based on an improved bacterial foraging optimization(IBFO) algorithm is proposed to deal with the non-convexity problem in the solution space or the global optimal unreachable problem caused by multiple constraints in the process of parameter optimization of a flight control system. The sampling method based on the lattice criterion is used to search the solution space as widely as possible. The niche technology based on K-means clustering is used to make multiple populations search their own space respectively and obtain as many feasible solutions as possible in different regions of the solution space. At the same time, the adaptive depth search strategy is used to ensure the robustness of the algorithm in the whole optimization process. The proposed algorithm can explore the feasible region of each solution in the flight control system and the relationship between them. Also, the proposed algorithm can reveal the characteristics of the solution space to a certain extent. Finally, the simulation results show that the proposed algorithm can effectively simplify the process of system parameter tuning, and obtain a flight control system that meets the design performance expectations more conveniently.