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The task of determining sun sensor placement on a spacecraft is generally performed manually, based on prior experience and similarity to previous designs. This type of manual process tends to be highly iterative, consumes valuable time and resources, and generally produces sub-optimal results. Because of blockage due to avionics boxes, solar panels, etc., there may be many locations on the spacecraft that are unsuitable as sensor locations. As the complexity of the spacecraft increases, this problem becomes increasingly difficult. This paper explores the solution of the multi-objective optimization problem that includes as constraints minimizing the number of sun sensors, providing continuous 4π steradian coverage, providing multiple sensor field-of-view overlap everywhere, and minimizing sensor blockage. The sun sensor placement problem is a difficult combinatorial optimization problem that cannot be solved to optimality in polynomial time due to the vastness of the solution space. Global search techniques that use a stochastic engine to explore diverse regions of the solution space (such as genetic algorithms) have been employed with great success against such problems. This paper examines the utility of implementing a sun sensor placement optimization tool using a hybrid genetic algorithm. This algorithm combines a genetic algorithm with another stochastic optimization technique known as simulated annealing. A candidate algorithm is presented, used to place sun sensors on a candidate spacecraft, and the results documented. The utility of the algorithm is assessed, and recommendations are made for additional enhancements that would increase algorithm performance and make the algorithm more suitable for actual applications.