A mathematical model is formulated for the multi-objective software project scheduling problem,aiming to optimize both the project duration and employee satisfaction.The model takes into account practical factors such as employee skill level and task importance,and matches important tasks with employees of higher skill ratings.Subsequently,a hyper-heuristic algorithm based on Q-learning is proposed to solve the model.In this algorithm,global search of the task-employee matrix is performed based on the matrix crossover operator and Jaya operator with random jitter,and local exploitation strategies are designed using problem-specific information to reduce project duration and increase employee satisfaction.Global search operators,neighborhood parameter values,and local exploitation strategies are combined to form eight Low-Level Heuristics (LLHs).Furthermore,a high-level strategy based on Q-learning is introduced to adaptively select appropriate low-level heuristics for populations in different evolutionary states,according to the historical performance of the LLHs.Experimental results show that the proposed algorithm outperforms representative algorithms in terms of Hypervolume Ratio (HVR) and Inverted Generational Distanced (IGD) on most of test cases.