With the increased use of Unmanned Aerial Vehicles (UAVs), it is envisioned that UAV operators will become high level mission supervisors, responsible for information management and task planning. In the context of search missions, operators supervising a large number of UAVs can become overwhelmed with the sheer amount of information collected by the UAVs, making it difficult to optimize the information collection or direct their attention to the relevant data. Novel decisionsupport methods that account for realistic operator performance will therefore be required to aid the operators. This paper considers a decision support formulation for sequential search tasks, and discusses a non-preemptive scheduling formulation for a single operator performing a search mission in a time-constrained environment. The formulation is then generalized to include operator performance obtained from previous human-in-the-loop experiments, and presents one of the principal contributions of the paper. The sensitivity of the proposed model is analyzed in the presence of uncertainty to the operator model and search times, and a comparison is made between the expected performance difference between this scheduling system and a greedy scheduling strategy representative of operator planning. The paper concludes with the design of a human-in-the-loop experiment for a scheduling, replanning task for a simulated UAV mission.