A Large-Scale Minimalist Multi-Robot System (LMMS) is one composed of a group of robots each with limited capabilities in terms of sensing, computation, and communication. Such systems have received increased attention due to their demonstrated range of capabilities and beneficial characteristics, such as their robustness to environmental pertubations and individual robot failure and their scalability to increasingly larger numbers of robots, not to mention the benefit of the relative simplicity of the individual robots. Little work has been done in investigating ways to endow such a LMMS with the capability to achieve a desired division of labor over a set of dynamically evolving concurrent tasks, a necessity in any task-achieving LMMS. Such a capability can help to increase the efficiency and robustness of overall task performace as well as open new domains in which LMMS can be seen as a viable alternative to more complex solutions. In this paper we present a method by which to achieve a desired division of labor in a LMMS, experimentally validated in a realistic simulation, and demonstrate its potential to scale to large numbers of robots and its ability to adapt to environmental pertubations.