The optimization problem in petroleum engineering revolves around optimizing the locations of production and injection wells in a way that maximizes oil production and safeguards oil reservoirs against potential risks. This optimization is typically pursued with the goals of enhancing productivity, reducing costs, and aiding oil operators in critical decision-making processes. In this study, the optimization of well placement and economic evaluation has been conducted within the water injection process. To achieve this, three optimization algorithms have been employed: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (HGAPSO). Net Present Value (NPV) calculations were performed based on the best optimized parameters, considering costs and profits derived from oil production. The best NPV was calculated.Simulation results demonstrate that the PSO algorithm outperforms GA and HGAPSO algorithms, making it a robust and effective method for optimizing well placement and determining the number of production and injection wells in the oil injection process. Each of these algorithms employed different methods to address the problem, leading to more optimal results based on various reservoir characteristics and operational conditions. Economic evaluation and the comparison of algorithms in scenarios without the use of PSO, GA, and HGAPSO indicated that when optimizing the placement of only 4 production wells, the PSO algorithm resulted in a 124.12% higher profit than natural production. Similarly, the GA algorithm achieved a 124.12% higher profit, and the HGAPSO algorithm demonstrated a 123.45% higher profit compared to natural production.In cases focusing solely on optimizing the placement of 2 injection wells, the PSO algorithm achieved a 118.6% higher profit than natural production. Likewise, the GA algorithm achieved a 118.58% higher profit, and the HGAPSO algorithm resulted