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The load balancing scheduling is the core of the load balancing technology in the cluster system. The actual load of servers will increase suddenly before the load value is updated if many clients link the servers in a short period. A mathematical model of load balancing was improved and an adaptive load balancing optimization scheduling based on genetic algorithm was proposed, analyzed and simulated. Empirical results show that the algorithm can reduce effectively the average execution time of all requests and speed up the average response time. Meanwhile, with the increment of the cluster size, the algorithm running time is not increased significantly while maintain good performance.