The advancement of space technology has led to a continuous increase in the number of Resident Space Objectives (RSOs), while the observation resources remain relatively limited, posing higher demands on the resource allocation of Space Surveillance Network. Currently, research has primarily focused on the observation of Unmanned Aerial Vehicles or maintenance of RSO catalog, with insufficient study on the criteria for objective allocation prioritization or the cooperative observation of high-precision tracking RSOs. This paper introduces a resource allocation method based on task-priority’s time-density, aiming to optimize the efficiency of observation task allocation and the accuracy of RSO tracking. Through simulation experiments, the traditional “start-time first” and “priority first” algorithms are compared, and the results show that the task priority’s time density algorithm performs better in terms of computational time, task completion rate, and facility idle rate.