This study developed a simulation/optimization model based on artificial neural networks and genetic algorithm techniques for solving conjunctive use of groundwater problems. The model simultaneously addresses all significant flows in a dynamic hydraulically connected stream-aquifer system. It also addresses multiple objectives. The genetic algorithm is a search procedure based on the mechanics of natural selection and genetics. It has been applied almost exclusively to single objective optimization problems. Water resources projects are generally constructed to serve multiple objectives. The nondominated sorting genetic algorithm was adopted for multiobjective optimization. The artificial neural network was used to represent simulation constraints inside the optimization model. It predicted groundwater system responses to changes in decision variables. The linked ANNGA model was able to solve complicated nonlinear reservoir- stream-aquifer system equations. A fuzzy-penalty function method was used to implicitly handle constraints on state variables. Three conflicting objectives were considered: (1) maximization of the sum of unsteady groundwater pumping and surface water diversions for three consecutive 60-day periods, (2) minimization of operating costs, and (3) maximization of hydropower production. The three objective functions were constrained by 21 state variables. Water use from one multipurpose water reservoir, two pumping wells, two stream diversions, and one reservoir diversion was optimized. Three scenarios were investigated. The first scenario optimized the first two objectives simultaneously. The second scenario maximized total water diverted and minimized pumping costs concurrently. The third scenario was a combination of the first two and optimized the three objective functions. The genetic algorithm control-parameters were investigated. A crossover probability of 0.6 and a niche size corresponding to 200 peaks were most appropriate for all tested scenarios. Mahfoud's population sizing method was investigated and validated. Population sizes for the three scenarios were obtained accordingly. The 21 artificial neural networks estimated state variables within a linear correlation range of 0.85 and 0.99. The final product of all three scenarios was a set of trade-off curves that allow the manager to assess the relative importance of each objective. The new simulation/optimization model was robustly able to simulate water flows and optimize different water management problems without violating any of the specified constraints.