Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton for parallel EMAS computations, written in Erlang. The skeleton enables us to capture a wide variety of concrete evolu- tionary computations that can exploit the same underlying parallel implemen- tation. We demonstrate the use of our skeleton on two different evolutionary computing applications: i) computing the minimum of the Rastrigin function; and ii) solving an urban traffic optimisation problem. We show that we can ob- tain very good speedups (up to 142.44× the sequential performance using 244 threads on a 61-core accelerator) on a variety of different parallel hardware, while requiring very little parallelisation effort.