A considerable amount of research into effective parallelization for discrete event driven simulation has been conducted over the past few decades. However, most of this research has targeted the parallel simulation infrastructure; focusing on data structures, algorithms, and synchronization methods for the parallel and distributed simulation kernels. While this focus has successfully improved and refined the performance of parallel discrete event simulation kernels, little effort has been directed toward analyzing and preparing the simulation model itself for parallel execution. Model specific optimizations could have significant performance implications, but have been largely ignored. This fact is complicated by the lack of a widely used simulation and modeling language for many domains. The lack of a common language is, however, not entirely insurmountable. For example, the partitioning and assignment of objects from the simulation model onto the hardware platform is generally performed by the simulation infrastructure. While partitioning can have dramatic impacts on the communication frequencies between the concurrently executed objects, most existing parallel simulation infrastructures do little to address this opportunity. This thesis addresses the partitioning and assignment of objects within a simulation model for parallel execution. The specific target of this effort is to develop a partitioning and assignment strategy for use in the WARPED parallel simulation kernel that has been developed and maintained at the University of Cincinnati. The focus of the work is to develop a general purpose solution that can function for any simulation model that has been prepared for execution on the WARPED kernel. The specific solution exploits a sequential kernel from the WARPED project to pre-simulate the simulation model to obtain profile data regarding the frequency of events communicated between objects. This event frequency data is then used to develop partitions to minimize the amount of event exchanges between the objects in the different partitions. This partition information is then used during the initialization sequences of the WARPED kernel to assign each partition i to a unique processing node in the parallel cluster. This method is independent of the simulation model and compute platform. Experimental results with existing simulation models from the WARPED project show that this method can achieve up to a six-fold improvement in run time over the naive partitioning algorithm that was previously used by the WARPED kernel.