The results of protein search engines depend mainly upon a set of parameters that adjust the searching space. One of the most effective parameters is the peptide mass window tolerance (w). Most of the current search engines use a constant user-defined value for this parameter. As an alternative option, Comet search engine designers proposed a statistical technique to estimate the best tolerance window for an input spectra file. However, this technique sometimes fails in picking a value, may set the parameter to a value that results in a loss of many correct matches, and is available only for one type of mass; namely ppm. In this paper, we propose to use particle swarm optimization (PSO) to improve the coverage of search engines by picking the optimal value for this influential parameter to maximize PSMs. Our results show that this biologically-inspired algorithm can be utilized to find peptide mass window tolerance values that facilitate Comet to increase peptide spectra matches, resulting in improved peptide identification. We also show experimental evidence that an open search (i.e., wide tolerance window) does not always optimize spectra matching using the current search engines and that narrow tolerance windows improve the coverage of protein search engines.