  OpenMOLE is a scientific workflow engine with a strong emphasis on workload
distribution. Workflows are designed using a high level Domain Specific
Language (DSL) built on top of Scala. It exposes natural parallelism constructs
to easily delegate the workload resulting from a workflow to a wide range of
distributed computing environments. In this work, we briefly expose the strong
assets of OpenMOLE and demonstrate its efficiency at exploring the parameter
set of an agent simulation model. We perform a multi-objective optimisation on
this model using computationally expensive Genetic Algorithms (GA). OpenMOLE
hides the complexity of designing such an experiment thanks to its DSL, and
transparently distributes the optimisation process. The example shows how an
initialisation of the GA with a population of 200,000 individuals can be
evaluated in one hour on the European Grid Infrastructure.
