Task scheduling plays a critical role in cloud computing and is a key factor affecting the performance of cloud computing. It has been proved to be an NP problem. Heuristic algorithm is one of the most effective methods to solve this problem. This paper focuses on the Biogeography-Based Optimization (BBO) algorithm, which serves in recent years as a new heuristic algorithm. Because the BBO algorithm converges slowly in the solution process, by combining Particle Swarm Optimization (PSO) algorithm, we propose a novel task scheduling algorithm, named Hybrid Migrating Biogeography-Based Optimization (HMBBO). A comparison experiment using Makespan as the objective function is performed on the Cloudsim cloud simulation platform. The experiment results show that, compared with several classical heuristic algorithms, HMBBO has  the advantages of strong optimization ability, fast convergence speed and high-quality solution, and provides a new way to solving the task scheduling problem in cloud computing environment.