For the sake of settle the disputes of algorithm poor optimization performance and slow training speed of gray wolf optimization algorithm, a new gray wolf optimization algorithm is proposed. In the initialization part of the algorithm, the reverse learning strategy is used to generate ordered gray wolf individuals, which validly ameliorates the convergence speed of the algorithm. The search ability of the algorithm is coordinated by designing a new nonlinear convergence factor and optimizing the individual location update strategy to reduce the probability of falling into local optimization. The elite selection retention strategy and tournament selection strategy are introduced to accelerate the population evolution and improve the convergence speed of the algorithm. The basic function test results and track planning simulation experiment verify that the new gray wolf optimization algorithm has strong astringency and high optimization accuracy, and the average track generation value spent by the algorithm is 19.9% less than that of gray wolf optimization algorithm.