: The optimization process is considered as maximizing or minimizing a predefined objective function using a structured algorithm under predefined limitations. In optimizing well- placement procedure, this function is defined in exploration and exploitation stages, separately and needs to include several effective factors such as geological, petrophysical and economical parameters simultaneously. Due to large quantity of involved parameters and the uncertainty associated with some of them, application of intelligent optimization methods such as evolutionary algorithms is inevitable. Optimizing the placement of new wells in an oil field is essential if productivity is to be maximized. The computational demands for this problem are substantial, as many function evolutions are required and each entails a full reservoir simulation (though surrogate models can be used in some cases). It is therefore essential that underlying optimization algorithm be efficient and robust. Used approach in this thesis is Particle Swarm Optimization (PSO) which is one of the mentioned algorithms. This algorithm is a relatively new approach for global optimization. The algorithm attempts to mimic social interactions exhibited in animal groups, e.g., flocks of birds in flying. Like GA, PSO consists of population of solutions, here referred to as particles rather than individuals. In our problem the particles are blocks of oil field. In this thesis the exploratory objective function was defined as the multiplication of the 3D reservoir Porosity, Estimation variance and Permeability (PEPr) in one of the south oil field reservoirs. The Particle Swarm Optimization (PSO) algorithm was then applied on the defined objective function throughout the defined search space which was specified by the extent of 3D Kriging estimations. The optimum well locations given by PSO algorithm for the first three priorities were cross validated through analyzing their PEPr function values. The results show that obtained optimum value for objective function, 71.2955, is in maximum range of objective function values in the studied area. The high facility of PSO and its ability to find extermum targets of objective function in well placement problem approved in this chapter are another quality of proposed method. In Exploitation and producing stage of Oil reservoirs, the Net Present Value (NPV) was defined as the appropriate objective function for the available data from other south oil field reservoirs. The Particle Swarm Optimization (PSO) algorithm was then applied on the predefined objective function throughout the search space encompassing all possible locations as the potential oil wells. The optimum well-placements given by PSO algorithm for six priorities were cross validated through analyzing their NPV function values. The results show that the obtained mean value for objective function for all six proposed well-locations is 431.850 which is placed among maximum of the NPV values in the range of 280.8 to 438/41. Finally the sensitivity of the proposed well locations as a function of the production rate was assessed and the results were found to be very consistent and stable if the increment or decrement of overall production is distributed evenly among all production wells.