To solve the problems of low efficiency, not to consider collision and poor applicability of the current robot path planning method for spraying entities with complex structure, a discrete grey wolf optimizer algorithm for solving multilayer decision problems was proposed and applied to the above path planning problem. In order to transfer the grey wolf optimizer algorithm with continuous domain to discrete grey wolf optimizer algorithm for solving multilayer decision problems, the matrix coding method was used to solve the coding problem of multilayer decision problem, a hybrid initialization method based on prior knowledge and random selection was proposed to improve the solving efficiency and precision of the algorithm, the crossover operator and the two-level mutation operator were used to define the population update strategy of the discrete grey wolf optimizer algorithm. In addition, the path planning problem of spraying robot was simplified to the generalized traveling salesman problem by the graph theory, and the shortest path model and path collision model of this problem were established. In the path planning experiment, compared with particle swarm optimization algorithm, genetic algorithm and ant colony optimization algorithm, the proposed algorithm has the average planned path length decreased by 5.0%, 5.5% and 6.6%, has the collision time reduced to 0, and has smoother paths. Experimental results show that the proposed algorithm can effectively improve the spraying efficiency of spraying robot as well as the safety and applicability of the spraying path.