In order to solve the Multi-Skilled resource-constrained Project Scheduling Problem (MSPSP), this study proposes an improved genetic algorithm. First, based on the mathematical model of the problem, a priority-based real number encoding method is established, and the objective function is converted into a fitness function for subsequent fitness calculations. Next, the niche technology based on group sharing is incorporated into the selection process of the genetic algorithm. In addition, with the help of deterministic sampling selection and subpopulation adjustment, the search ability of the algorithm is further improved. Then, gene repair and multiple verification mechanisms are introduced in the crossover and mutation operations to enhance the algorithm’s optimization ability. Finally, the overall process of the algorithm is given. The effect of the algorithm on the iMOPSE data set shows that the improved genetic algorithm is an effective method for solving MSPSP problem, and it has a sound reference significance for the study of related practical problems.