Integration testing is an indispensable step in the software testing process. In response to the problem of how to rationally sort the classes in the system in integration testing, researchers worldwide have proposed a variety of methods to generate class integration test orders. However, most of them didn’t take the stubbing complexity as the indicator, which is an important factor in evaluating the test cost. In order to solve this problem, this study proposes a method of generating a class integration test order based on reinforcement learning, using the overall stubbing complexity as the indicator to evaluate the test cost, and generating a class integration test order with the stubbing complexity as low as possible. First, the reinforcement learning task is defined, and the pursuit goal of the algorithm is set according to the task. Second, the static analysis of the program is performed and the stubbing complexity is calculated according to the results of the analysis. Then, the calculation of the stubbing complexity is integrated into the design of the reward function to provide information and basis for choosing the next action. Finally, the value function is fed back through the reward function, and the value function is set to ensure that the cumulative reward is maximized. When the agent completes the specified number of training times, the system will select the class integration test order that obtains the largest cumulative reward value for output, which costs the lowest stubbing complexity pursued. The experimental results show that the results obtained by this method are better than those obtained by other existing methods in terms of the overall stubbing complexity as the evaluation indicator.