In order to solve the problems of long period and low efficiency in microwave antenna design, a multi-objective microstrip patch antenna automatic design and optimization method combined with machine learning was proposed. In this paper, by using genetic algorithm to optimize the initial weights and thresholds of the neural network model, the optimized GA-BP model was used to predicts the multiple groups sets of antenna parameters on the resonance point of |S11|, the effective area below -10 dB and the corresponding reward value. Given the electromagnetic response of the target antenna, the geometric parameters of the antenna can be also predicted by the GA-BP model. The results show that the determination coefficient R2 predicted by BP model is about 0.968, while the GA-BP model proposed in this paper is as high as 0.994, which is significantly better than the traditional BP neural network model.