In order to solve the problems of long running time, low searching efficiency and frequent deadlock in the path planning of ant colony algorithms, this paper proposes an ant colony algorithm based on the Darwin's theory of evolution. Firstly, a simple mode of the ant colony algorithm is proposed to solve the problem of blind search in blank grids. Then, in order to improve the global search ability and avoid falling into deadlock, the target influence factor and obstacle influence factor are introduced into the heuristic function. Finally, the pheromone updating rules of ant colony algorithm are improved using the Darwin's theory of evolution to accelerate the iteration speed and shorten the running time of the algorithm. Experiments on raster maps of different scales show that the evolutionary ant colony algorithm proposed in this paper can speed up the iteration speed, improve the search efficiency, achieve the optimal path and avoid the deadlock.