Interplanetary missions to far-off planets are expensive both in terms of launch cost as well the time of flight. In such cases, multiple gravity assist (MGA) has proved advantageous to obtain an optimal trajectory to such targets, as demonstrated by missions like Voyager-1 and 2, Galileo, Cassini, etc. Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), etc. have been extensively used for the optimization of MGA trajectories. However, the benchmark MGA problems under the European Space Agency’s GTOC (Global Trajectory Optimization Competition) are tough even for evolutionary algorithms. These benchmark problems are derived from actual missions, namely, Cassini, Messenger, and Rosetta. Hybrid optimization techniques are combinations of an evolutionary algorithm with a local search method offering the advantage of both methods. This paper evaluates various schemes of hybrid optimization (GA/DE along with BFGS, pattern search, and Nelder Mead) to optimize the benchmark problem Cassini 1 proposed under GTOC. The effectiveness of the hybrid schemes is evaluated based on their ability to reach the optimal result and the number of function evaluations.