This paper addresses the problem of designing a fused scalar objective function for autonomous surveillance–target search and tracking (S&T)–by unmanned aerial vehicles (UAVs). A typical S&T mission includes multiple, most often inherently conflicting, objectives such as detection, survival, and tracking. A common approach to coping with this issue is to optimize a fused scalar objective–a convex combination (weighted sum) of the individual objectives. In practice, determining the fusion weights of a multiobjective combination is, more or less, a guesswork whose success is highly dependent on the designer’s assessment and intuition. An optimal (trade-off) point in the performance space is hard to come up with by varying the weights of the individual objectives. In this paper the problem of designing optimal fusion weights is treated more systematically in a rigorous multiobjective optimization (MOO) framework. The approach is based on finding a set of optimal points (Pareto front) in the performance space and solving the inverse problem–determine the fusion weights corresponding to a chosen optimal performance point. The implementation is done through the known normal boundary intersection (NBI) numerical method for computing the Pareto front. The use of the proposed methodology is illustrated by several case studies of typical S&T scenarios.