Cloud computing has become the focus information processing and many related fields with its powerful computing and storage capacity. For some of the existing phenomenon about the large calculation and high computing time cost in cloud task scheduling algorithms based on the batch model and evolution algorithm, this paper presents a local cloud task scheduling algorithm based on improved GEP and change of resources. In the process of designing the algorithm, it is first improved the GEP algorithm according to the characteristics of cloud task scheduling. And then, this paper constructs a fitness function considering both the comprehensive utilization and energy consumption. Finally, this paper constructs a local cloud task scheduling algorithm based on improved GEP and comprehensive utilization. The algorithm proposed in this paper reduces the computing time cost by monitoring the physical resource usage and reducing the number of physical machines involved in the task scheduling. The comparison experiments among GEP, genetic algorithm and the algorithm proposed in this paper based on RH (rolling horizon) model has been made. The results show that the proposed algorithm can not only reduce the optimization time, hard to fall into the local optimal solution, but also has the faster convergence speed.