Cloud computing is increasingly becoming a popular solution to massive data analysis in bioinformatics. In order to enable scientists to harness the computing power provided by Cloud platforms, we designed Green Pipe, a scalable computational workflow system, which runs jobs as MapReduce tasks on virtual Hadoop clusters. This paper introduces a power-aware scheduling algorithm in the workflow engine to optimize workflow execution in terms of running time and energy consumption. Experimental results demonstrate the performance improvement in Green Pipe.