Phylogenetic trees are commonly reconstructed based on hard optimization problems such as Maximum parsimony (MP) and Maximum likelihood (ML). Conventional MP heuristics for producing phylogenetic trees produce good solutions within reasonable time on small databases (up to a few thousand sequences) while ML heuristics are limited to smaller datasets (up to a few hundred sequences). However, since MP and ML are NP-hard, application of such approaches do not scale large datasets. In this paper, we present a promising divide-and-conquer technique, the TAZ method, to construct an evolutionary tree. The algorithm has been implemented and tested against five large biological datasets ranging from 5000-7000 sequences and dramatic speedup with significant improvement in accuracy (better than 94%), in comparison to existing approaches, has been obtained. Thus, high quality reconstruction can be obtained for large datasets by using this approach. Moreover, we present here another approach to construct the tree dynamically (when sequences come dynamically with partial information). Finally Combining the two approaches, we show parallel approaches to construct the tree when sequences are generated or obtained dynamically.