<p>The growing size and complexity of data demand scalable evolutionary methods. Genetic Algorithms (GAs), with their adaptability and natural parallelism, are well suited to this challenge. This survey reviews Distributed Parallel Genetic Algorithms (DPGAs), examining how core components such as fitness evaluation, selection, crossover, and mutation are parallelized across distributed environments. We organize the literature by implementation strategy, including custom distribution topologies, MapReduce models, Spark-based frameworks, and virtualization-based approaches, with recent work also exploring platforms such as Flink, Dask, and Ray. The survey highlights architectural patterns, performance trade-offs, and practical challenges, while consolidating recurring limitations such as fault tolerance, communication overhead, and configuration sensitivity. By linking algorithmic strategies to distributed architectures, the paper provides both a foundation for understanding the state of the art and a roadmap for future DPGA research, emphasizing the need for scalable, robust, and adaptive frameworks for large-scale data analysis.</p>