The goal of this research is to develop a formal methodology for long-term sampling and monitoring at intrinsic bioremediation sites. Intrinsic bioremediation couples the ability of indigenous microbial activity to decay contaminants with long-term site sampling and monitoring to insure regulatory compliance. This methodology combines optimization and simulation to choose sampling locations that quantify the mass of contaminant while minimizing monitoring costs. It has three primary components: (1) groundwater fate-and-transport simulation, (2) geostatistical interpolation and global mass estimation, and (3) monitoring plan design using a genetic algorithm (GA). Contaminant concentrations at all potential monitoring locations are predicted using the Reactive Transport in 3-Dimensions (RT3D) simulation package. Kriging subroutines are then combined with a GA to search for sampling plans that accurately describe the contaminant mass in the plume at minimal cost. For each sampling plan, the RT3D output is used by the kriging subroutines to estimate contaminant concentrations at all unsampled locations within the domain and the total mass of contaminant. Results show that this methodology is effective at both reducing sampling costs and accurately quantifying the mass of contaminant in the plume. The effects of various GA parameters on model performance are also presented. Extensions of this work in the future will include exploring the efficacy of alternate plume interpolation schemes, incorporating uncertainty, and testing other types of GAs.