Several techniques currently exist for fitting orbits of directly imaged exoplanets. One aspect that still needs improvement is the ability to fit multiple planets with limited prior information about which planet is which, to enable an accurate prediction of orbital parameters based on only a few observations. Since data from direct observations is expected to consist of multiple objects at each epoch, looking at each epoch separately is not sufficient to decide whether 1) a detected object is part of an exosolar system and 2) which planet it corresponds to. Existing multi-planet trajectory matching libraries, such as “Orbits For The Impatient” (OFTI), currently require the user to specify which data points belong to which planet. This assumes that the user has already matched true-positive detections to planets. Additionally, this planet matching between detected objects needs to be taken into account when assessing the impact of observation scheduling on the accuracy of trajectory estimation. To address this need for fitting orbits to multiple objects with limited knowledge, we propose an approach that uses a Monte Carlo study of different observation schedules and planetary systems. For each case we automatically match observations to planets and check the accuracy of the match. By considering a large number of such cases, we provide constraints on the number of observations and their spacing necessary to “deconfuse” the detections. We are developing an algorithm for rapidly matching a large number of detections to a small potential number of planets. The algorithm invokes rejection sampling to select a trajectory, which explains the observation with the smallest number of trajectories, and that trajectory is then refined using OFTI. We present preliminary planet matching success rates for several observation schedules based on existing planetary system data sets and assess the accuracy of trajectory fitting combined with OFTI.