The scheduling problem has been studied extensively in the literature. Many algorithms have been developed to operate with different types of processors and tasks. In the robotics domain, when considering each robot as a processor, some of these algorithms can be directly adapted. However, most of the existing algorithms can only handle single-robot tasks, or multi-robot tasks that can be divided into single-robot tasks. As the task requirements may only be partially known, and the available (heterogeneous) robots can dynamically change, robots may be required to cooperate tightly to share different capabilities (i.e., sensors and motors). In such cases, considering scheduling for individual robots is no longer sufficient, since the robots need to work at the coalition level. Although there exist a few algorithms that also support these more complex cases, they do not represent efficient solutions that can be adapted by various multi-robot systems in a convenient manner. In this paper, we propose heuristics to address the multi-robot task scheduling problem at the coalition level, which hides the details of robot specifications, thus allowing these heuristics to be incorporated straightforwardly. These heuristics are easy to implement and efficient enough to run in real time. We provide formal analyses and simulation results to demonstrate and compare their performances.