The use of swarms such as unmanned aerial vehicles to solve problems is becoming more prevalent around the world. One promising application is drones in Search & Rescue operations. The modeling and simulation of these scenarios could improve the success rate and efficiency of those operations and in the case of Search and Rescue, help save lives. This research created a testbed that will serve as a model of swarm operations under variable conditions, designed for a use case of drone Search & Rescue. Using this simulation to study the effects of swarm paths, environmental conditions, and other variables will lead to a further understanding of what impacts they have on the outcome of the swarm operations. The prototype tests have one goal; to decrease the time spent before finding the target. Preliminary results from the simulation show that the saturation point wherein adding more drones stops reducing the time to find the target is around 3.2 drones per square mile. Faulted agents are added to the model to hinder the swarm’s operation by producing both false positives and false negatives. Future work includes adding and improving to the functionalities of the simulation, as well as implementation and testing of consensus algorithms.