Many interesting problems in genetic epidemiology are formulated as non-linear optimization problems using the Gemini/Almini library of routines. Because of the wide availability of networked workstations, we investigate cost-effectively improving the performance of the Gemini/Almini library by exploiting parallelism with a set of workstations connected via a local area network. Instrumentation of the Gemini/Almini optimization routines reveals significant potential for improving performance via parallelism. Using these instrumentation results, we identify promising targets of parallelism and discuss two preliminary implementations that demonstrate the potential benefits of cost-effective parallel implementations. By applying parallelism to the Almini/Gemini routines, we hope to potentially improve the performance of a large number of genetic epidemiological applications.