A set of different methodologies are developed for multiple objective management of coastal aquifers. The coastal aquifer management models are developed using a numerical simulation model, meta-model, and the multiple objective optimization algorithm NSGA-II. The NSGA-II algorithm is also modified to accommodate initial solution generation using the Latin hypercube sampling for uniform sampling in bound space. These initial solutions are useful to improve the efficiency of the optimization algorithm. One important issue in developing management models for coastal aquifers incorporating the density dependent flow and transport processes is the computational feasibility. A few variations of the management model are also evaluated to test the potential for improving the computational efficiency. The variations in the formulation of management models include: direct linking of numerical simulation model, introducing meta-models [in this study artificial neural network (ANN)] in place of original numerical simulation model, using partially trained meta-model as a screening model for specifying initial solutions for the numerical simulation model linked optimization model. The developed models are capable of obtaining the nondominated front of the multiobjective management model without solving the modified single objective model iteratively. Also, the meta-model approach is shown to be computationally efficient in generating the nondominated front, especially when a partially trained (ANN- based) meta-model is used as an initial screening model for the nondominated front search process. These developed methodologies are tested for an illustrative coastal aquifer study area. The performance evaluations show potential applicability of developed methodologies for multiobjective management of saltwater intrusion in coastal aquifers.