Water requirements in agricultural production sector have increased in recent years. This necessitates the adequate management of limited water resources. Since agriculture is the main water consumer, finding proper methods and models for the allocation of water to farm lands is vital to the management of available water.  The goal of this study is to find ways to optimize the allocation of water to the farms in water scarcity condition, using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and to compare their capabilities. First, the needed data was generated and prepared using analysis functions of GIS. Then, the water attainable from several resources and water required by different farms were computed. Afterwards, objective function was calculated using the land area, the crop price and yield response factor. The allocation of water to lands was optimized such that the total economic profits of all farms were maximized. The profits resulted from PSO were slightly about 106938976 Rial higher than GA. In addition, the convergence of PSO was much faster than GA. The repeatability test showed higher stability of PSO (The variance of the normalized values for GA and PSO are 0.151 and 0.104 respectively. In two different scenarios, termination conditions are considered as to reach a specified run number and to reach a defined accuracy of answers. For both scenarios, the execution times of PSO were less than GA (320 and 272 seconds correspondingly). In general, PSO performance is better than GA regarding all evaluation criteria. The only drawback of PSO is that it allocates no water to some of the farms. In other words, the algorithm suggests that for maximizing the economic revenue, some of the crops and farms should be left without irrigation.