The dynamic planning of the training airspace is of great significance for improving the utilization rate of the airspace, improving the efficiency of military training, and alleviating the contradiction between military and civilian air. In this paper, the spatial dynamic programming problem is processed in stages, and the total occupation time is minimized by the optimal scheme of each stage. Aiming at the dynamic programming problem in each stage, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization algorithm is proposed. By integrating the crossover and mutation ideas in the genetic algorithm, the DPSO algorithm"s ability to get rid of the local optimal solution is improved, and the convergence speed and accuracy of the algorithm are improved. In order to ensure the diversity of the population, the adaptive crossover operator and mutation operator are designed. Finally, the gantt chart is used to represent the whole airspace planning process. Compared with the genetic algorithm, the improved gpso is applied to the numerical example