In this paper, we investigate a method for simultaneously scheduling multiple space surveillance sensors that has the potential to exploit measurement level sensor fusion to improve orbital state estimation accuracy. State error covariance information is used to predict the influence independent and combined measurements will have on estimation accuracy during a prescribed scheduling period. The resulting information provides a means of determining the best sensor combination to observe each object of a population at the most beneficial time. We present a comparative study of multi-sensor scheduling over independent sensor scheduling by means of numerical simulation. We find that there is a delicate balance to be maintained between the higher statistical effectiveness of simultaneous observations and the total number of objects that can be observed during a scheduling period.