Small Unmanned Aerial Vehicles (UAVs) are revolutionizing area coverage operations such as Search and Rescue (SAR) or surveillance and monitoring. However, maximizing the search efficiency in large areas with limited battery life and uncertain target locations remains a challenge. This paper aims to maximize the likelihood of finding the target as quickly as possible by introducing a novel formulation of the Coverage Path Planning problem that takes into account the energy constraints of a small UAV and prior information about the target area, employing a global optimization algorithm, specifically Simulated Annealing, to generate a path. The proposed algorithm can be applied to scenarios such as SAR, where the Area of Interest (AOI) cannot be fully covered by the UAV in a single flight due to its limited endurance, and where the expected target position is modeled by a Probability of Containment (POC) map. The algorithm prioritizes visiting the areas where the POC is high in order to increase the probability of detecting a target and reduce the time at which it is detected, which in SAR represents a higher likelihood of survival. The algorithm presented demonstrates superior performance compared to a baseline Boustrophedon algorithm typically used for area coverage.