Intelligent Wells (IWs) actively manage oil production and water breakthrough. IWs comprise Inflow Control Valves (ICVs), which are automated valves with multiple openings. In reservoirs with geological uncertainties, ICVs can be used to optimize production in different segments of IWs. However, economic uncertainties related to the varying oil prices and the high capital cost of IWs also need to be examined. The objective is to maximize the Net Present Value (NPV) of a horizontal Intelligent Well (IW), undergoing geological and economic uncertainties, by changing IW architecture and number, flow control position, and placement of Inflow Control Valves (ICVs), while satisfying safety standards, equipment capacity constraints and minimizing water breakthrough. To simulate the pressure drops for different scenarios within the IW for the optimization problem, a mechanistic model of a Conventional Wellbore (CW) and ICVs has been considered for oil-water flow. Nested loops were employed to maximize the objective function. The inner loop optimized the placement and flow control position with a genetic algorithm. The outer loop optimized the number of ICVs and IW's physical dimensions with a gradient-based method. Validating with experimental data resulted in 30.1% root mean squared error. The Pressure, Volume and Temperature (PVT) properties for oil and water were determined with empirical correlations. Sensitivity analysis indicated that the number of ICVs is the most essential variable among all variables for maximizing NPVs. The presented optimization strategy is successful at increasing NPVs relative to conventional wells. Unlike conventional literature, current optimization strategy not only focuses on design or production of IW, but it also considers both, the design and the IW's production optimization simultaneously.