Aiming at the problem of insufficient dynamic adaptability of existing methods to the terminal airspace of multiple military airports,through a comparison of opertational differences between military and civil aviation,this paper proposes redundancy design and dynamic adjustment mechanisms to constitute a multi-objective optimization model with dual goals, i.e.traffic flow balancing and minimization of total flight path length through making a comparison between military and civil aviation operations.On the basis of the traditional Particle Swarm Optimization (PSO) algorithm, this paper designs an improved Multi-Objective Particle Swarm Optimization algorithm (MOPSO) by integrating the local optimization mechanism of Simulated Annealing (SA), the non-dominated sorting strategy of NSGA-II, and initialization using Logistic chaotic mapping.Furthermore, the entropy weight-TOPSIS method is employed to conduct a comprehensive evaluation and selection of the solution set, ultimately determining the optimal arrival and departure fixes planning scheme.The results show that compared with the conventional Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, this proposed improved algorithm achieves an average improvement of 23% approximately in the Hypervolume metric, and the Inverted Generational Distance metric and the Spacing metric decrease by approximately 45% and 39%respectively.This method can effectively balance the contradiction between traffic balance and flight path optimization, providing a scientific basis for the efficient planning of military aviation terminal airspace in multi-airport areas.