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This paper proposes a large-scale resource scheduling method based on improved genetic algorithm combined with secondary coding. This method is used to solve the problem that traditional genetic algorithm can not meet the resource scheduling problem in large-scale cloud computing environment under multi-user. In the selective replication phase, a dual fitness function based on minimum task completion time and matching degree is used to screen the populations by double criteria. Next, the cross-mutation probability of the algorithm is adaptively optimized, and its adaptive ability is further improved, which ensures that the algorithm converges to the optimal solution as soon as possible. Finally, the improved genetic algorithm (IGA) is analyzed on the CloudSim platform. It shows that the improved genetic algorithm can be well applied to large-scale resource scheduling, and the result is better than the comparison algorithm.