Advancement in Unmanned Aerial Vehicles (UAV) technology supervised us to use them in many situations like seismic survey of an area, border and restricted area surveillance, disaster rescue, agriculture monitoring, and many others. The deployment of UAVs for expansion and extension of wireless network coverage for surveillance and rescue during and post disaster situations is fenced with promising challenges. The dense user coverage, quality of service (QoS), user data rate requirement, limited short flying time, and optimal trajectory path are some of the pertinent issues that UAVs are encountering. In this work we develop some algorithms for fast deployment of UAVs for application in disaster scenarios and optimal trajectory of each UAV in some specified area. The main aim of the work is to reduce the time complexity for optimal deployment of UAVs in order to optimize diverse parametric constraints. We propose a highly time efficient algorithm for UAV deployment through Lloyd and FCM as the initial localization of position in conjunction with the evolutionary algorithm namely Differential Evolution (DE) and Hybrid Differential Evolution with Learning (HDEL) for finding the optimal location of UAVs. We also develop an algorithm for finding out the optimal trajectory to reach the intended location for effective deployment of UAVs to ensure optimal resource allocation and user coverage. Comprehensive simulation of various performance measuring metrics is obtained and the result shows that the proposed algorithms are well efficient as compared to some of the standard algorithms used in deployment of UAVs.