Load balancing is an important means to improve resource utilization and system stability. Based on Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm, a new task scheduling model and strategy about load balancing for cluster in heterogeneous cloud environment were proposed. In order to maximize customer satisfaction degree and reduce the total execution time of a collection of tasks under ensuring the system load as much balanced as possible, a concept of user bias degree on cluster node performance such as safety and reliability and a method of grasping the degree of preference on security and reliability of cluster nodes and estimating the load information of the tasks were added into the design of scheduling policy. The simulation shows that the improved AMPSO algorithm performs better than the original AMPSO algorithm and the basic Particle Swarm Optimization (PSO) algorithm at convergence speed and the capacity of jumping out the local optimum. The results prove that the improved AMPSO can better improve the profit margins of the cloud service provider while ensuring the load balancing of the cluster.