Molecular evolutionary and phylogenetic analyses are critical to understanding evolutionary biology and functional genomics. However, as sequencing technologies become more affordable and databases expand, the computational demands for analyzing large sequence datasets continue to rise. These challenges—ranging from long computation times to memory constraints—limit the accessibility and scalability of phylogenetic inference, especially for researchers relying on desktop computers. In this presentation, I will discuss algorithmic, statistical, and heuristic approaches my group has developed to address these challenges. We have developed novel approaches that dramatically reduce the computational resources required for key phylogenomic tasks, such as selecting the best-fit substitution model, conducting bootstrap analyses, and estimating evolutionary parameters like substitution rates, branch lengths, and divergence times. By reducing memory footprints, our methods enable greater parallelization, improving performance on both personal computers and high-performance computing systems. We validate new approaches and their implementations through simulations and real-world datasets to ensure high accuracy while gaining dramatic computational efficiencies, bridging the gap between theoretical advances and practical tool development in MEGA. Ultimately, we hope to contribute to more sustainable, accessible, and reproducible phylogenomics, democratizing participation in evolutionary research and accelerating discovery.