One of the most fascinating aerospace problems today is the optimisation of interplanetary trajectories in the Solar System, using gravitational fly-bys in order to reduce fuel consumption and/or mission duration. This is a complex optimisation problem due to the complexity of the models and the lack of analytical solutions, as well as to the presence of strong discontinuities. An exhaustive search of the space of input variables is unaffordable even with modern, state-of-the-art computing technology. Thus, a feasible approach requires artificial intelligence and modern optimisation techniques based on the intelligent selection of some potential solutions. These individuals evolve in order to generate better solutions until some optimisation criteria are met. In this work, two evolutive algorithms are considered, both based on particle swarm optimisation. In both cases, the trajectory to be optimised departs from Earth and, after a fly-by in Mars, arrives to Jupiter. In the single-criteria case, only the fuel consumption is considered as a variable to be minimised, while in the multi-criteria case the fuel consumption and the total time of the mission are simultaneously taken into account, building a Pareto-front of non-dominated solutions that provides an interesting view of the possible options for the space mission. In both, the single- and multi-criteria cases, the swarm algorithms optimise some tuning parameters of the trajectory: launch epoch, and times of flight between planets. Results are compared to other works on the same problem. They demonstrate the benefit of applying these evolutive techniques to decrease both mission duration and propellant cost when using intermediate gravity assist bodies.