The current military trend toward many diverse platforms and sensors available for use within a surveillance environment requires the ability to efficiently and effectively task these sensors. This results in a requirement for functionality within the surveillance problem for sensor resource management. This functionality requires the automatic generation of appropriate tasks, the mapping of these tasks to a set of feasible sensors, the calculation of the benefit achieved for executing the task, and the eventual optimal scheduling of these tasks. As part of a recent research effort, we have developed a closed loop sensor resource management environment. As part of this simulation testbed environment we have addressed two key problems. The first is the development of genetic algorithm approach for solving the sensor scheduling problem. Our approach solves a sensor scheduling problem involving multiple sensors as well as several constraints related to scheduling time windows, resource limitations, and linked/repeating tasks. The second area of development is the automatic generation of the tasks to be scheduled. This automated task generation includes the generation of tasks for different missions which in our problem include both surveillance as well as high priority task requests. In each case, our task generation capability creates a sensor independent score that is used in the scheduling algorithm. This paper will describe the sensor management problem in general as well as give a description of our genetic algorithm scheduling approach. We will also describe our approach for generating tasks for multiple missions and the generation of the corresponding task benefit. We will conclude with a discussion of the results obtained during our effort and directions for future research.