Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial to distributed multi-agent systems, where agents are required to communicate and cooperate with others. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is able to infer the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide when and with whom to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions according to the sub-goals. We demonstrate the idea in a typical target-oriented multi-agent task, namely multi-sensor target coverage problems. The experiments show that the proposed model not only outperforms the state-of-the-art methods in sample efficiency and target coverage rate but also has good generalization across different scales of the environment.