In order to reduce the cost of scientific workflow execution in cloud environment, we propose an approach to optimizing the execution plans of scientific workflows in cloud environment. It introduces the monkey group algorithm and relies on the intra-level and inter-level optimization of the current execution plan. Under the premise of ensuring the global deadline of the workflow, through the logical aggregation of the same-level tasks and the inter-level adjustment of the tasks, the difference in the number of tasks at each level is minimized to avoid waste of resources and reduce the waiting time of tasks. Experiments show that compared with the BTS algorithm and the SPSWVC algorithm, the proposed method can reduce resource consumption and the total delay time of tasks.