  Due to recent booming of UAVs technologies, these are being used in many
fields involving complex tasks. Some of them involve a high risk to the vehicle
driver, such as fire monitoring and rescue tasks, which make UAVs excellent for
avoiding human risks. Mission Planning for UAVs is the process of planning the
locations and actions (loading/dropping a load, taking videos/pictures,
acquiring information) for the vehicles, typically over a time period. These
vehicles are controlled from Ground Control Stations (GCSs) where human
operators use rudimentary systems. This paper presents a new Multi-Objective
Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving
a team of UAVs and a set of GCSs. A hybrid fitness function has been designed
using a Constraint Satisfaction Problem (CSP) to check if solutions are valid
and Pareto-based measures to look for optimal solutions. The algorithm has been
tested on several datasets optimizing different variables of the mission, such
as the makespan, the fuel consumption, distance, etc. Experimental results show
that the new algorithm is able to obtain good solutions, however as the problem
becomes more complex, the optimal solutions also become harder to find.
