The evolution of color categorization systems is investigated by simulating categorization games played by a population of artificial agents. The constraints placed on individual agent’s perception and cognition are minimal and involve limited color discriminability and learning through reinforcement. The main dynamic mechanism for population evolution is pragmatic in nature: There is a pragmatic need for communication between agents, and if the results of such communications have positive consequences in their shared world then the agents involved are positively rewarded, whereas if the results have negative consequences, then involved agents are punished. A mechanism for changing the composition of the population due to agents’ birth and death is also investigated. This birth-death mechanism is found to effectively move an established population color naming system toward a theoretically more optimal one. The simulation results of this article provide insights regarding mechanisms that may constrain universal tendencies in human color categorization systems observed in the linguistic and anthropological literatures.