The chapter considers the problem of multidimensional global unconditional optimization, call basic problem. One of the ways to solve this class problems is to use population algorithms. Population algorithms are metaheuristic, i.e. algorithms for which convergence to a global solution has not been proven. The effectiveness of population algorithms largely depends on their structure and the selected parameter values. Multi-criteria meta-tasks of parametric, structural and structural-parametric synthesis of the “best” popular algorithm for solving the basic problem are set. The scheme of the proposed method for solving the meta-task of structural-parametric synthesis is presented (including the tasks of structural and parametric synthesis). The purpose of the research presented is to evaluate the effectiveness of the proposed method using the example of one class of population algorithms for global optimization. For a set of three test multi-modal functions and one class of population algorithms of global optimization a wide computational experiment was performed. The average number of iterations and the probability of localizing the global minimum of the objective functions were chosen as the performance criteria. The results of this experiment are presented. They are showing the prospects of the method.