BP (formerly British Petroleum) incurs significant costs associated with monitoring subsurface remediation sites. The purpose of this project is to evaluate whether these costs could be reduced by identifying and eliminating both spatial and temporal redundancies in the monitoring data at a BP site without significantly increasing monitoring errors. The project also aims to demonstrate the potential for multi-objective optimization approaches to improve monitoring decision making at the many sites at BP and elsewhere with long-term monitoring records. The first step in the optimization process is to identify monitoring objectives and constraints, and express them in mathematical form. In this case, the initial objectives were to minimize the number of samples collected and to minimize relative BTEX interpolation error. The BTEX interpolation error for trial sets of sampling plans are calculated by comparing the concentrations interpolated using all sampling locations and times with those interpolated using only reduced sampling frequencies or locations. Historical data from the wells that are currently being sampled are used to develop a suite of interpolation models, which are then tested using a cross-validation approach. Adaptive Environmental Monitoring System (AEMS) software, developed at the University of Illinois and RiverGlass Inc., is then used to search through the billions of sampling plans to identify the optimal tradeoffs between the number of samples collected and the relative error. Copyright ASCE 2005.