In order to assure software quality and assess software reliability, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products in the past three decades. In principle, two widely used methods for the parameter estimation of SRGMs are the maximum likelihood estimation (MLE) and the least squares estimation (LSE). However, the approach of these two estimations may impose some restrictions on SRGMs, such as the existence of derivatives from formulated models or the needs for complex calculation. Thus in this paper, we propose a modified genetic algorithm (MGA) to estimate the parameters of SRGMs. Experiments based on real software failure data are performed, and the results show that the proposed genetic algorithm is more effective and faster than traditional genetic algorithms.