Transplantation is a recent phenomenon. Its big developments took place in the past few decades and it was an alternative to save several lives. Kidney is the most popular among all transplantable organs. This work aims to formulate an optimization model in order to transport and better allocate kidney living donors in a 12-month horizon across the entire North American kidney transplantation network. According to OPTN, there are currently 97.550 patients in the waiting list. The proposed model seeks to minimize inherent costs to this planning and reduce a percentage of the accumulated queue. In order to characterize and model this problem appropriately, this study used statistical and linear programming tools widely known in the literature. The Poisson distribution was selected to model the arrival of patients, living and deceased donors, besides the usage of Monte Carlo simulation to analyze this problem s uncertainties. The first-stage decision is based on the amount of routes to be created between 12 regions in the country to transport living donors through hypothetical contract signings with 6 airlines. The second stage proposes to quantify the transport flow of living donors, besides accounting an artificially high penalty cost when the reduction queue target is not achieved (default occurrence). Such model was divided into two case studies: (i) a deterministic model, considering the expected values of the Poisson random variables; (ii) a stochastic model, looking upon patients’ demand and deceased and living donors’ supplies under uncertainty. The results indicate that, for a queue reduction target a= 1 percent, in the deterministic approach, 9 routes were established for a cost of US 11,205 million, transporting 1037 living donors throughout the year without any penalty incurred. The plan s total cost was US 44,536 million. For the stochastic case, simulating 200 scenarios on Julia, 26 routes were established for US 38,810 million, transporting 853.9 donors in average, despite the default of 50.1 living donors in average. The total cost (excluding the artificial penalty cost) was estimated in US 67,263 million. Lastly, fixing the 9 routes predefined by the first case study and combining these with demand and supplies uncertainties for 200 scenarios, this resulted in a total cost of US 42,947 million with a high default of 83 percent among the generated scenarios. The region represented by Los Angeles was found to be the most critical regarding probability of default.