This study aimed to optimize cropping patterns, irrigation water allocation and maximization of stakeholders’ profit in different weather conditions. To work towards these goals, a nonlinear programming model was developed. The model included two optimization algorithms with an integrated soil/water balance to define the optimal reservoir release policies and cropping pattern. The proposed model was solved using the Genetic and Cuckoo Algorithms. The results showed that in normal weather conditions, wet, dry, hot and dry, under the cultivation area and profit from new cropping patterns provided by the two algorithms has significantly increased compared to the current crop pattern. Following the new cropping pattern can result in an extreme diminution of water use in this sector. The final results from the two algorithms were very close together, and both methods achieved the global optimum answer, but what makes primacy of the Cuckoo to the Genetic algorithm was the high-speed convergence of the Cuckoo algorithm, which means the algorithm cuckoo with less repetition converged to the best answer.