warms of Unmanned Aerial Vehicles (UAVs) are nowadays used in various domains, e.g. precision agriculture, military and photography. In some precision agriculture applications, the drones must cooperate and maintain connectivity to collect data from a group of ground sensors. Hence, the drones trajectories must be planned such that objectives like trajectory length or energy minimization together with connectivity must be satisfied. A popular approach in the literature is to first generate the trajectories offline and then follow these trajectories online using Model Predictive Control. In large networks, we may not want to share all the data among all the agents, in particular the ones that are far, a distributed system appears to be the best solution. In this work, the multiple agents trajectory optimization problem is formulated via MPC (Model Predictive Control) and solved by a distributed algorithm using the Python package DISROPT. In this framework, at each time step, each agent of the network formulates a local optimization problem which is interconnected to the others through coupling constraints which describe the connectivity maintenance. Preliminary simulation and experimental results show promise for the proposed approach.