In this paper, a new hybrid GSA-GA algorithm is presented for the constraint nonlinear optimization problems with mixed variables. In it, firstly the solution of the algorithm is tuned up with the gravitational search algorithm and then each solution is upgraded with the genetic operators such as selection, crossover, mutation. The performance of the algorithm is tested on the several benchmark design problems with different nature of the objectives, constraints and the decision variables. The obtained results from the proposed approach are compared with the several existing approaches result and found to be very profitable. Finally, obtained results are verified with some statistical testing.