In order to give full play to the performance of the missile, it is necessary to optimize the design of multiple parameters such as missiles mass and flight sequence angle under the conditions of meeting technical and tactical indicators.Genetic Algorithm (GA) is very effective for multi-parameter optimization problem.In this process, ballistic integration is needed to calculate the range of all individuals in the population, but the model of ballistic integration is complex, which is not conducive to global search.This paper uses Back-Propagation Neural Network (BPNN) to fit the process of numerical integration when calculating the range, improves the individuals fitness calculation in GA, and completes the design of the missiles overall parameters under the given range index.The simulation results show that: the computational accuracy of the trained neural network meets the requirements, the computational speed of the improved GA is significantly increased, and its local search capability is strengthened.