Time is critical during search and rescue operations, as human survival diminishes exponentially if survivors are not located and recovered efficiently. This thesis sought to integrate technologies into a solution that helps rescuers plan for a mission utilizing multiple autonomous unmanned systems for search operations. It exploits methods of image analysis to fuse data into a common map and identify key areas of search interest. The key mission areas were developed by comparing edge detection techniques on images obtained from remote sensing platforms in the DigitalGlobe database. Together with close-up snapshots of the environment obtained from drones, three-dimensional maps were developed by stitching the images together into a comprehensive model for a mission commanders use. With the mission bubbles developed, a probabilistic road map was used to develop an optimal trajectory to the search area. It was found that by connecting to the 20 nearest neighboring points in the K-dimensional graph instead of all the points, and using the weighted heuristic method for the A* search, formed the most optimal means to obtain a solution. Together with a tool to generate search patterns for multiple drones, an experiment at Camp Roberts was conducted successfully. Technology was effectively used in the development of a mission-planning tool utilizing a set of heterogeneous unmanned systems for a search mission, which can be expanded for various types of military applications.