  This paper introduces a new method of partitioning the solution space of a
multi-objective optimisation problem for parallel processing, called Efficient
Projection Partitioning. This method projects solutions down into a single
dimension, greatly reducing the cost of partitioning the search space. We test
EPP on a variety of randomly generated multi-objective combinatorial
optimisation problems. The results are compared with the state of the art in
parallel partitioning, and we show that in all scenarios tested, our new
algorithm performs significantly better. Our proposed method allows the
generation of non-dominated sets of larger problems with more decision
variables or objective functions through the use of highly parallel
computational infrastructure. Source code is provided to allow others to
utilise, build upon and improve the algorithm
