Abstarct: Most of the optimization algorithms that are baxsed on numerical linear and nonlinear programming methods, require the derivative information of the objective function and use those information for searching the range of solution in the approximately near the starting point. Because of that, there are limitations in usage of these algorithms for optimization problem in natural world. If there is more than one local optimum in the problem, the final result may depend on the value of starting point, and the obtained optimal solution may not necessarily be the global optimum. Therefore, in recent decades, application of mextaheuristic algorithms for optimization has been attracted researcher’s attention. Because there is no need for heavy mathematical calculation, and having capability of reaching to the global optimal point, or at least near of that, these innovative searching methods, which are baxsed on natural processes, are used to solve various kinds of problems. Due to the significance role of optimization problems in engineering sciences use of mextaheuristic algorithms for the solution of them, this thesis, by utilizing innovative algorithm methods, such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), Harmony Search (HS) and Charge System Search (CSS), simultaneously proposed a innovative method for optimization of cross section of truss structures. By the way, the designing space of problem are investigated properly and the desired optimum solution are approximately achievable. However, by designing many different examples, the result from other algorithms and this proposed way, which is called multi-search method baxsed on metheuristic algorithms, have been analyzed and the applicability of this method also demonstrated.