The challenge in search and rescue is to identify the optimal paths when searching the entire location. This is further complicated by the unknown and yet complex environmental terrain; whilst being under the pressure of time. Many of the existing search algorithms such as Depth First Search (DFS) are focused on having only a single agent to sweep through the location. Drawing inspiration from the self-organisation mechanism and the emergence of global behaviour through local interactions between agents in swarm intelligence; this study utilises the information exchange between agents in the swarm to navigate a search area effectively. We demonstrate the proposed swarm-based search method and compare its performance against the existing path finding algorithm Breadth First Search (BFS) on terrains with different complexity. We conducted simulations of search and rescue operations; with findings that the proposed Swarm Intelligence Based Search Strategy (SIS) is able to reach upwards of 95% the effectiveness of BFS with approximately one-fifth the cost of BFS. In addition, a thorough analysis and experimental results to show the optimal number of agents is shown. Our results also demonstrate that having more agents do not necessarily lead to better traversal.