Birds nest in multifunctional semi-natural environments. Intensification of agriculture and forestry prevents their successful breeding, threatening globally their survival. Early bird detection allows for targeted conservation actions, such as local (temporary) habitat protection. The conservationist thus looks for at detecting priority bird species as soon as a territory is occupied, for instance using acoustic surveillance network. We present a comprehensive method to optimize acoustic coverage with a minimum number of sensors in the network. Our method includes a sound propagation model and algorithms for optimized sensor placement. Relevant parameters (e.g., topography, soil type, height of vegetation, weather, etc.) for the sound propagation model are automatically extracted from an area of interest. We implemented and compared Particle Swarm Optimization and Genetic Algorithms-based approaches to solve the optimisation problem.