In the United States, renal complications rank eighth as the most common cause of death, creating waiting lists approaching \(100\,000\) people, with only around \(25\,000\) accessing kidney transplants. Faced with the challenge of meeting the total demand for necessary kidneys, renal exchange programs involving donors, recipients, and altruists have emerged, aiming to establish exchanges with chains or shortened cycles for logistical reasons. With the increasing prevalence of these exchanges and programs in both the United States and Europe, this paper presents a genetic algorithm designed to address them. This strategy is conceived to tackle larger instances, overcoming the limitations of conventional mathematical models reported in the literature. The application of this metaheuristic provides an effective and scalable solution, improving kidney allocation in the context of high demand and logistical constraints, offering responses to instances that cannot be addressed exactly.