The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a
disaster. A proper plan to better cover the needs of the affected people within the disaster area while
maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a
set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the
earliest stage of the emergency operations. While the drone provides the flexibility required to visit
subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the
deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work
proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping
points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA)
which has proven to be helpful in finding good solutions in short computing times. We provide experimental
analysis on the factors effecting the performance of the output solutions, around an illustrative test instance.
Results show the applicability of these techniques for providing proper solutions to the studied problem