<p>Cloud computing has become a widely used computing paradigm in recent years. In this paradigm, the data are stored in cloud datacenters which has led to concerns about data security and privacy. There is a need to devise effective security measures for securing data. Many researchers have worked to address security concerns using different methods. The methods include traditional cryptography, DNA-based cryptography and combinations of different methods. The methods either suffer from security issues, computational complexity or scalability. This article presents a Parallel Genetic Algorithm (ParaCryptoGA) for data encryption to improve security and efficiency. The algorithm uses block encryption to efficiently use parallel processing. The proposed approach consists of an algorithm for cryptographic key generation and data encryption. First, key is generated using GA based key generation method. The data is divided into blocks, and each block is encrypted separately. During this process, the data is processed using genetic operators, improving randomness and security. Logical operations are performed between the data and the generated key, resulting in final ciphertext. This block-wise encryption is performed in parallel to ensure efficiency while maintaining security. Different datasets are used to validate the proposed algorithm. The effectiveness of the proposed encryption method is measured using traditional security parameters. The proposed algorithm is compared with other methods. Both theoretical and experimental results show that the proposed algorithm has better performance in execution time, throughput, Avalanche effect, and resistance to brute-force attacks.</p>