In recent years, due to the rapid development of the Chinese economy, consumers have put forward higher requirements for the delivery time of goods in online shopping, which has intensified competition among various logistics enterprises. Considering the current difficulties in selecting the optimal path, this project plans to adopt a intelligent logistics automated distribution path method based on binary grey wolf optimization algorithm. This article improves the convergence speed of the algorithm by introducing binary methods and adaptive cross mutation strategies. The improved binary grey wolf optimization algorithm is used in logistics path optimization experiments. The average path length of genetic algorithm is 18.6m, with an average computation time of 25.3ms. The average path length of binary GWO (Grey Wolf Optimize) is 21.1m, with an average computation time of 15.2ms. Through experiments, it has been proven that the binary grey wolf optimization algorithm can optimize the route of logistics delivery vehicles.