Emerging spatial/mobile crowdsourcing service platforms enable workers (i.e., crowd) to complete spatial crowdsourcing tasks (e.g., taking photos, verifying data on-site, delivery) that are tagged with rewards, time and location features. In this paper, we develop online route recommendation service for a mobile crowdsourcing worker, such that he can (i) reach his destination on time and (ii) receive the maximum reward from spatial crowdsourcing tasks along the route. We show that no online algorithm can compute the optimal route. Then, we propose effective heuristics to compute routes with high reward, and present efficient techniques to accelerate their computation. Experimental results on real datasets show that our proposed heuristics incur low response time and produce high-reward routes (yielding 80 percent of the optimal reward for on-site tasks and 70 percent of the optimal reward for delivery tasks).