Glowworm Swarm Optimization (GSO) is one of the optimization techniques, which need to be parallelized in order to evaluate large problems with high-dimensional function spaces. There are various issues involved in the parallelization of any algorithm such as efficient communication among nodes in a cluster, load balancing, automatic node failure recovery, and scalability of nodes at runtime. In this paper, we have implemented the GSO algorithm with the Apache Spark framework. The Spark framework is designed in such a way that one does not need to deal with any parallelization details except the logic of the algorithm itself. For the experimentation, two multimodal benchmark functions were used to evaluate the Spark-GSO algorithm with various sizes of dimensionality. We evaluate the optimization results of the two evaluation functions as well as we will compare the Spark results with the ones obtained using a previously implemented MapReduce-based GSO algorithm.