The use of drones is recently gaining particular interest in the field of search and rescue. However, particular skills are still required to actively operate in a mission without crashing the drone. This limits their effective and efficient employment in real missions. Thus, to assist the rescuers operating in stressful conditions, there is a need to detect an increase of workload that could compromise the outcome of the mission. In this work a simulator is designed and used to induce different levels of cognitive workload related to search and rescue missions. Physiological signals are recorded and features are extracted from them to estimate cognitive workloads. The NASA Task Load Index is used as subjective self-report workload reference. Then, performance is recorded to objectively evaluate the execution of the tasks. Finally, the analysis of variance (ANOVA) is used to verify intra- and inter-subject variability. Results show statistical decrease of the mean normal-to-normal (NN) interval with an increase of cognitive workload. Moreover, it is observed a decrease of performance while an increase of cognitive workload exists. This information can be used to detect the need for assistance.