The world is now facing a continuous increase in people with kidney failure and increasing deaths due to this disease. It was necessary to work on this serious problem. When kidneys fail to perform their functions, only two solutions are available for the patient, either dialysis or kidney transplantation. Dialysis doesn’t provide a permanent solution for renal kidney failure and regular painful follow-ups are mandatory; on the other hand, kidney transplantation offers a life-long cure. It enables patients to live in a way closer to their normal lives. But it isn’t always that easy for patients to find compatible donors; this can cost them many years on waiting lists, and sometimes they die while waiting. This chapter aims to resolve kidney exchange problems optimally by utilizing various bio-inspired optimization techniques by determining the maximum groups of compatible donors/recipients from the registry of incompatible pairs submitted as transplant candidates. Chemical reactions, whales, and ant lions optimization algorithms are proposed to quickly find the optimal solution of the kidney paired donation and a maximum number of kidney transplants based on existing donor-patient pairs. Roulette and rank-based candidate selection methods are applied with the optimization algorithms to speed up the candidate selection in case of a massive pool of patients and donors. The experiments used a Seidman generator to generate six instances as reference groups. We only accept incompatible donor-patient and altruistic donor pairs and do not accept a patient without any donor, as evident from the data set. The results show that the ant lion optimizer with rank selection method is beneficial against the other two optimizers.