Crowdsourcing systems (CS) are platforms that enable users, called requesters, to publish tasks that others, called workers, are expected to accomplish. Usually, these are systems where workers perform tasks using desktop computers. Recently, some CS have appeared with location-based tasks that require a worker to be at a given location within a given time window to be accomplished. In this paper, we study the problem of matching these location-based tasks and mobile workers under a novel perspective where three actors (workers, requesters and the system itself) of the CS may benefit differently from a configuration of tasks and workers. We introduce the Trajectory Recommendation Problem (TRP) where a CS finds a trajectory (sequence of tasks) for a single mobile worker that maximises the satisfaction of one or more of the actors. We show that TRP is NP-complete and then propose an exact algorithm for solving it. Our experiments have proved that our algorithm is a feasible solution when up to a few hundred tasks must be analysed to find an optimal solution. Finally, we propose an architecture for the deployment of the recommendation in a CS.