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Abstract. Virtual machine scheduling is the process of selecting the most suitable server in cloud data centers to deploy newly created VMs. The optimal placement is important for improving resource utilization and reducing resource wastage in a cloud computing environment. In this article, we propose an Self-Adaptive Particle Swarm Optimization for the virtual machine scheduling problem. Our algorithm focuses on efficient VM allocation to physical servers in order to minimize the total resource wastage and the number of servers used. Simulation experiments were designed to evaluate the proposed algorithm with performance and scalability. Its solution performance are compared with PSO and GPSO scheduling strategies. The results show that the proposed algorithm is more efficient and effective than the methods we compared to.