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Achieving network connectivity and user coverage are among the most important objectives in Wireless Mesh Networks (WMNs). These objectives are formulated as optimization problems, which unfortunately cannot be solved to optimality due to their computational hardness nature. Heuristic methods have thus been considered for such optimization problem aiming to compute near optimal solutions in reasonable amount of time. One family of heuristic methods known for their efficiency is that of local search algorithms. The methods in this family explore the solution space through a path of solutions, visited during the search process. Among methods of this family, Tabu Search (TS) has shown its superiority due to advanced mechanisms to overcome getting stuck into local optima. In this paper we present the implementation and evaluation of TS for the problem of mesh router node placement in WMNs, formulated as a bi-objective optimization problem. The optimization model consists in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage. The experimental evaluation showed the efficiency of TS in solving a variety of problem instances generated using different distributions for the mesh client nodes in WMNs.