Resource scheduling in cloud computing has long been the focus of research. In this paper, particle swarm algorithm is introduced into the cloud computing and aiming at the shortcomings of this algorithm like fast local convergence and being easy to fall into local optimal value. Two improvements are made in this paper: one is to introduce differential genetic algorithm into particle swarm algorithm while finding the optimal solution, which can not only give play to particle swarm's advantage of quick global searching speed, but also give play to differential genetic algorithm's advantage of efficient local researching while combining the advantages of these two algorithms and making up for the deficiency of particle swarm algorithm. Another is to introduce penalty function so as to avoid particles moving towards void space and save costs of moving. Cloudsim platform shows that algorithm in this paper can effectively meet resource scheduling in cloud computing while having great improvement in reducing time of completing the task as well as consumption of costs so as to provide a reference for resource scheduling in cloud computing.