Directed evolution is generally regarded as a useful approach in protein engineering. By subjecting members of a mutant library to the power of Darwinian evolution, desired protein properties are obtained. Numerous reports have appeared in the literature showing the success of tailoring proteins for various applications by this method. Is it a one-way track that protein practitioners can only learn from nature to enable more efficient protein engineering? A structure-and-mechanism-based approach, supplemented with the use of reduced amino acid alphabets, was proposed as a general means for semi-rational enzyme engineering. Using human GST A2-2*E, the most active human enzyme in the bioactivation of azathioprine, as a parental enzyme to test this approach, a L107G/L108D/F222H triple-point mutant of GST A2-2*E (thereafter designated as GDH) was discovered with 70-fold increased activity, approaching the upper limit of specific activity of the GST scaffold. The approach was further experimentally verified to be more successful than intuitively choosing active-site residues in proximity to the bound substrate for the improvement of enzyme performance. By constructing all intermediates along all putative mutational paths leading from GST A2-2*E to mutant GDH and assaying them with nine alternative substrates, the fitness landscapes were found to be “rugged” in differential fashions in substrate-activity space. The multidimensional fitness landscapes stemming from functional promiscuity can lead to alternative outcomes with enzymes optimized for other features than the selectable markers that were relevant at the origin of the evolutionary process. The results in this thesis suggest that in this manner an evolutionary response to changing environmental conditions can readily be mounted. In summary, the thesis demonstrates the attractive features of the structure-and-mechanism-based semi-rational directed evolution approach for optimizing enzyme performance. Moreover, the results gained from the studies show that laboratory evolution may refine our understanding of evolutionary process in nature.