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MegaBlast is one of the most important programs in NCBI BLAST (Basic Local Alignment Search Tool)toolkits. However, MegaBlast is computation and I/O intensive. It consumes a great deal of memory which is proportional to the size of the query sequences set and subject (database) sequences set of product. This paper proposes a new strategy for optimizing MegaBlast. The new strategy exchanges the query and subject sequences sets, and builds a hash table based on new subject sequences. It overlaps I/O with computation, shortens the overall time and reduces the cost of memory,since the memory here is only proportional to the size of subject sequences set. The optimized algorithm is suitable to be parallelized in cluster systems. The parallel algorithm uses query segmentation method. As our experiments shown, the parallel program which is implemented with MPI has fine scalability.