Crowdsourcing systems (CS) are platforms that enable a system or a user to publish tasks in order to be accomplished by others. Typically, a CS is a system where users, called workers, perform tasks using desktop computers. Recently, some CS have appeared with spatiotemporal tasks. Such tasks require a worker to be in a given location within a specific time-window to be accomplished. We propose and study here the usage of five heuristics for solving the NP-hard trajectory recommendation problem (TRP). In a TRP, the system recommends a trajectory to a worker that allows him to accomplish spatiotemporal tasks he has skill and/or affinity with, without exceeding his available time. Our experiments show that some of our heuristics are efficient alternatives for a heavy optimal approach providing trajectories with an average utility of about 60% of the optimal ones.