Until recently, a novel spatial crowdsourcing paradigm, namely Three-Dimensional (3D) spatial crowdsourcing, has emerged, in which the task requestors and the workers need travel to their designated third-party workplaces, e.g., shared offices, to deliver certain services, such as DiDi station ride-sharing service, Quyundong sport training service in the Online-To-Offline (O2O) applications. In 3D spatial crowdsourcing applications, a core issue is to develop an efficient global tasklist plan, based on the tripartite matching among the three parties, i.e., task requestors, workers and workplaces, which is different from the conventional spatial crowdsourcing. In this context, one key challenge is how to suitably schedule the available workers with the consideration of the interests of all the parties, under the constraint of worker resource. To answer the questions, in this paper, we propose and study a new problem, namely Social-Welfare-driven Task Scheduling (SWTS) problem, which strives to schedule the workers' continuous routines, i.e., successively implementing tasks for different requestors at different workplaces, to promote the social welfare for all the involved parties. We prove our studied problem is NP-hard, and devise two heuristic optimization algorithms to solve it. Finally, we conduct extensive experiments which verify the efficiency and effectiveness of the proposed algorithms on both real and synthetic data sets.