Location finding is considered as an NP-Hard problem, in which the complexity and volume of computations due to larger numbers of demand points and service centers are exponentially increased. To overcome such complexities meta-heuristic algorithms are usually used. These methods usually produce answers close enough to the optimum solution in acceptable time. In this research three Meta heuristic algorithms of Tabu Search (TS),Genetic Algorithm (GA) and Simulated Annealing (SA) are used to optimize the location of fire fighting station and allocating urban blocks to them. The mentioned Meta heuristic methods are evaluated and compared with respect to time scale, the value of objective function, number of iteration and coverage of the study area. Different scenarios are developed, based on the number of iterations and the initial population for the genetic method. Similarly, different scenarios are developed for annealing method based on the number of repetitions of the same movements and temperature. The Tabu Search method has the maximum time of computation and minimum value of objective function, among the Meta heuristic methods. In addition, the sites selected by Tabu Search provide the best coverage in the area studied. In simulated annealing method, the solution ideas are produced when the number of iteration is larger than the size of the problem. The achieved results, in second scenario of simulated annealing method, have proved the correctness of this claim. It could be concluded that the simulated annealing method can be a suitable method when quick time results are required. When both quality of solutions and performance are to be considered, the genetic algorithm can be a proper choice. Finally, when there is no time limitation, the Tabu Search can generate the best results.