In this thesis, we will address the problem of static wireless sensor network deployment. Our research aims to generate the best network topology in relation to the following objectives: i) the cost of deployment (number of sensors), ii) the quality of monitoring, iii) network connectivity, and iv) the network lifespan. The problem in hand requires multi-objective optimization and is NP-complete. To overcome the great complexity involved, we will propose several heuristic deployment strategies and we will tackle the problem in three stages. In the first stage, we will consider the cost of deployment and the quality of monitoring only. We will propose a new deployment strategy named the Differentiated Deployment Algorithm (DDA), based on image processing and 3D modelling. In the second stage, we will build on the work carried out in the first stage by introducing the network connectivity objective. This will lead us to propose two deployment strategies based on the Tabu Search metaheuristic. The first strategy is known as the Bernoulli Deployment Algorithm (BDA), and is a probabilistic strategy in which the decision to deploy or remove a sensor follows a Bernoulli distribution. The second strategy is known as the Potential Field Deployment Algorithm (PFDA). This is a deterministic method that draws heavily on robotic (virtual forces). Finally, in the third stage, all of the objectives will be studied together (i.e. the network lifetime objective will be also considered). The proposed final strategy is called the Multi-Objective Deployment Algorithm (MODA). It is based on Multi-Objective Tabu Search (MOTS) metaheuristic and virtual forces. Moreover, the obtained results outperform the related deployment strategies.