Accurate reconstruction of phylogenetic trees very often involves solving hard optimization problems, particularly the maximum parsimony (MP) and maximum likelihood (ML) problems. Various heuristics have been devised for solving these two problems; however, they obtain good results within reasonable time only on small datasets. This has been a major impediment for large-scale phytogeny reconstruction, particularly for the effort to assemble the Tree of Life - the evolutionary relationship of all organisms on earth. Roshan et al. recently introduced Rec- I -DCM3, an efficient and accurate meta-method for solving the MP problem on large datasets of up to 14,000 taxa. Nonetheless, a drastic improvement in Rec- I Rec-I-DCM3's-DCM3's performance is still needed in order to achieve similar (or better) accuracy on datasets at the scale of the Tree of Life. In this paper, we improve the performance of Rec- I -DCM3 via parallelization. Experimental results demonstrate that our parallel method, PRec-I-DCM3, achieves significant improvements, both in speed and accuracy, over its sequential counterpart.