Semi-flocking algorithms have been demonstrated to be efficient in maneuvering MSNs in multi-target tracking tasks. In many real-world applications, targets can be assigned with different priorities according to their importance of being tracked. However, existing semi-flocking algorithms normally assume the importance of all targets to be identical, which may not allocate resources in an efficient manner. In this paper, we propose a target evaluation method that incorporates priorities of the targets in the assessment process. Based on the evaluation results, mobile agents decide to track a target or continue to scan the terrain via a probabilistic task switching mechanism. Simulation results indicate a higher effectiveness of the proposed method in target tracking and area coverage when compared with two existing semi-flocking algorithms.