Existing approaches for sensor network tasking in space situational awareness (SSA) rely on techniques from the 1950s and limited application areas while also requiring significant human-in-the-loop involvement. Increasing numbers of space objects, sensors, and decision-making needs create a demand for improved methods of gathering and fusing disparate information to resolve hypotheses about the space object environment. This work focuses on the cognitive work in SSA sensor tasking approaches. The application of a cognitive work analysis for the SSA domain highlights capabilities and constraints inherent to the domain that can drive SSA operations toward decision-maker goals. A control task analysis is also conducted to derive requirements for cognitive work and information relationships that support the information fusion and sensor allocation tasks of SSA. A prototype decision-support system is developed using a subset of the derived requirements. This prototype is evaluated in a human-in-the-loop experiment using both a hypothesis-based and covariance-based scheduling approaches. Results from this preliminary evaluation show operator ability to address SSA decision-maker hypotheses using the prototype decision-support system (DSS) using both scheduling approaches.