In the face of increasing large-scale scheduling problems, the development of new algorithms becomes more and more important. A Q-Learning scheduling algorithm based on reinforcement learning is proposed for permutation flow shop scheduling problem. By introducing state variables and behavior variables, the scheduling problem of combinatorial optimization is transformed into sequential decision-making problem to solve the permutation flow shop scheduling problem. The proposed algorithm is used to test the Flow-shop international standard provided by OR-Library, and compared with some existing algorithms, the results show that the algorithm is effective.