Focusing on the higher ratio of processor utilization and lower execution cost of a scientific workflow in cloud, a policy of execution optimization based on task cluster aggregation was proposed. First, the tasks were reasonably replicated and aggregated into several clusters. Therefore, the key tasks could be scheduled as early as possible. Then, the task clusters were aggregated again to facilitate the spare time among the tasks in the task cluster. The experimental results show that the proposed policy can improve the parallelism of workflow tasks, advance the earliest finish time of the whole workflow and it has a significant effect in improving the utilization ratio of processors and lowering the cost of workflow execution.