In order to improve the efficiency of cloud com- puting task scheduling, the Improved Genetic Algorithm (IGA) and the Improved Particle Swarm Optimization (PSO) are integrated into the IGA-IPSO algorithm for cloud computing task scheduling. The fitness function is constructed by integrating the three objectives of task completion time, task execution cost and virtual machine load balancing to find the optimal solution of task scheduling. The particle swarm algorithm is improved and the dynamic inertia weight strategy is used to improve the adaptive search of the algorithm. In the early stage of task scheduling, the IGA algorithm is used to reduce the solution space, and the Improved PSO is used in the later stage of task scheduling to quickly converge to the optimal solution. Simulation experiments show that compared with the other algorithms, this algorithm has faster convergence speed and stronger optimization ability. In cloud computing task scheduling, it can not only reduce task completion time and execution cost, but also optimize virtual machines load.