Engineering the conformational stabilities of proteins through mutations has immense potential in biotechnological applications. It is, however, an inherently challenging problem given the weak noncovalent nature of the stabilizing interactions. In this regard, we present here a robust and fast strategy to engineer protein stabilities through mutations involving charged residues using a structure-based statistical mechanical model that accounts for the ensemble nature of folding. We validate the method by predicting the absolute changes in stability for 138 experimental mutations from 16 different proteins and enzymes with a correlation of 0.65 and importantly with a success rate of 81%. Multiple point mutants are predicted with a higher success rate (90%) that is validated further by comparing meosphile-thermophile protein pairs. In parallel, we devise a methodology to rapidly engineer mutations in silico which we benchmark against experimental mutations of ubiquitin (correlation of 0.95) and check for its feasibility on a larger therapeutic protein DNase I. We expect the method to be of importance as a first and rapid step to screen for protein mutants with specific stability in the biotechnology industry, in the construction of stability maps at the residue level (i.e., hot spots), and as a robust tool to probe for mutations that enhance the stability of protein-based drugs.