In this thesis we investigate the improved scheduling of parallel applications on workstation clusters using the Message-Passing Interface (MPI) software environment. MPI is a collaboration by many organisations to define a de facto message-passing standard. The original specification has only a simple scheduling mechanism for submitting parallel applications on networks of workstations. Demonstrators applications that are commonly employed in engineering and scientific fields have been parallelised using the MPI paradigm to provide assessment of the proposed mechanism to schedule effectively parallel applications. A research survey has been conducted to investigate the state-of-the-art of clustering systems including the facilities provided for programmers to execute parallel applications on networks of heterogeneous workstations. In addition, load balancing techniques have been evaluated to examine how these mechanisms can be applied to the workload distribution especially on heterogeneous workstation clusters. As a result from this study, a hybrid dynamic/static workload distribution mechanism described as selective load balancing is proposed as an original contribution to improve load balancing on heterogeneous workstation clusters. Finally, a novel lightweight approach is presented to extend the MPI specification when using networks of heterogeneous workstations which extends the existing concepts of the software environment. This approach targets uncontrolled configurations. The design philosophy of this new environment, called Selective-MPI (S-MPI), is described in detail. Enhanced results of the demonstrator applications using this system are compared with standard MPI. These results demonstrate the improved efficiency of S-MPI, with a reduction in elapsed-time of up to 38%.