mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.