Based on the research background of one-to-one near visual range air combat of unmanned aerial vehicles (UAVS), a new air combat decision-making method combining weighted matrix strategy and evolutionary algorithm with coarse and fine granularity optimization is proposed. Firstly, the UAV maneuver model and air combat situation assessment advantage function are established based on the actual combat situation. Secondly, considering that the discrete variable optimization based on maneuvering library and the continuous optimization based on maneuvering ability both have the problems of insufficient accuracy and high calculation cost, a maneuvering decision framework combining coarse and fine granularity optimization is proposed. At the level of coarse-grain optimization, the matrix game method is introduced, and the Kalman filter algorithm is used to adjust the weight of the traditional matrix game method to solve the problem of conservative choice of maneuvering weight reference. At the fine-grained optimization level, based on the differential evolution principle, further search for better maneuvers. The effectiveness of the proposed decision frame is verified by four sets of comparison experiments. The weighted matrix game algorithm based on trajectory prediction can take effective countermeasures against the enemy’s actions.