Persistent Space Situational Awareness (SSA) is one of the top priorities of the DoD. Currently the Space Surveillance Network (SSN) operates using only a task-based method. The goal of this thesis was to compare the current task-based space surveillance performance to a search-based method of space surveillance in the GEO belt region. The performance of a ground telescope network, similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) network, was modeled and simulated using AGI’s Systems Tool Kit (STK) and Python. The model compared search-based and task-based space surveillance methods by simulating 813 Resident Space Objects (RSOs) on the summer solstice, fall equinox and winter solstice. Four performance metrics for comparing the search-based and task-based methods were minimum detectable size, detection rate, coverage area, and latency. The search-based method modeled six different search patterns at varying starting positions. Results show that the minimum detectable size average for task-based was 47.6 cm in diameter while search-based methods ranged from 38.3 cm - 45.4 cm in diameter. Detection rate for task-based was 100% while the search-based ranged from 91.7% - 96.8%. Coverage area for task-based was 46% of the GEO belt and the search-based method ranged from 3.5% - 84.4%. Average latency (revisit time) for task-based was 78 minutes and search-based methods ranged from 62 - 469 minutes. It was found that task-based surveillance was the better method for current operational conditions by using a weighted decision criteria. However, as the number of RSOs increase there is a point at which the search method has better performance
