<p>The increasing reliance of humans on software systems has heightened the pressure on software organizations to meet end-user demand for high-quality software. To ensure software quality, continuous testing is necessary. However, prolonged testing can lead to increased software costs. To stay competitive, organizations are releasing software early and continuing testing during the operational phase, addressing existing bugs by releasing patches. A patch is a set of changes in a computer program designed to fix, improve, or update it. Patches are released to fix any security vulnerability or to fix any bug. For minor changes to any software, it is easy and economical to release patches to the user rather than releasing newly designed software. In this paper, we have introduced a software reliability growth model (SRGM) that integrates continuous testing and multiple patch release on multi-release software utilizing genetic algorithm (GA) to optimize patch release timing and cost. Our model emphasizes a realistic understanding of the modern software development lifecycle, where software is often released with known bugs that are fixed through subsequent patches. We have used the exponential distribution function to develop the model. Further, the parameter estimation is done using Statistical Package for Social Sciences (SPSS). We have also discussed the reliability of the proposed model. Numerical illustration is done on a real dataset in order to validate the results. Our model helps software organizations to maintain high-quality standards even after early software release. Continuous improvement and reliable patch management can enhance user satisfaction and trust in software products.</p>