The resource scheduling problem in cloud computing environment can be regarded as a multi-objective optimization problem. In this paper, we propose an optimal scheduling strategy for cloud computing resources based on improved genetic algorithm. The strategy framework includes key components such as coding strategy, fitness function design, selection mechanism, crossover and mutation operations. Using the Cloudsim experimental platform, resource optimization scheduling simulation experiments are conducted to combine multiple scheduling algorithms and compare the performance of resource optimization scheduling in different scenarios. The improved genetic algorithm is close to convergence after 60 rounds of iterations when executing multi-tasks, and the execution time is 17.66% to 53.65% shorter than the comparison algorithm. The algorithm allocates resources in a more balanced way and improves the computational efficiency. In terms of energy consumption, the improved genetic algorithm reduces 11.67%~28.38% than the comparison algorithm and has better CPU utilization. The total utility value of this paper’s algorithm increases gradually with the increase of the number of resources, and when the number of resources is 1000, the total utility value reaches 262.58. Multi-level demonstration of this paper’s algorithm has excellent performance, which can maximally satisfy the optimization of resource scheduling in cloud computing.