This paper develops a space-based, target search-to-tracking framework that incorporates an optical sensor model. The framework is used for analysis of dynamic steering of a space-based optical sensor to search, detect, and track unknown space objects that have highly uncertain states. The analysis with the target search framework compares derived information-theoretic and maximum probability target search algorithms to efficiently characterize a target with large uncertainty. The optical sensor model for the target search framework simulates a square, two-dimensional camera frame that provides measurements for the estimation process and includes a clutter model to represent false alarm. The target search framework is evaluated with Monte Carlo Simulations using estimated real-world case parameters and provides results that offer an efficient and optimized initial target search and estimation performance for SSA.