Usage of Image has been increasing and used in many applications. Some applications such as the transmission of images in computer and mobile environments cannot use images directly due to the large amount of memory space needed to store these images. Image compression has a very important role in digital image processing and for effective transmission and storing of digital images. There are various techniques that can be used in image compression. Today JPEG algorithm has become the de facto standard in image compression. The source of its excellent compression ability is the quantization table which determine which frequency components of the Discrete Cosine Transform (DCT) will be neglected. The JPEG default quantization table is generated from a series psycho-visual experiments from several angle points of experimental views. Particle Swarm Optimization (PSO) is a biologically-inspired optimization algorithm and has been experimentally demonstrated to perform excellent to solve many optimization problems by finding out the global best solution in a complicated search space. In this paper, to enhance the accuracy of the JPEG image compression algorithm, the PSO algorithm has been used to search the optimum quantization table. Simulation results show that the performance of the standard JPEG method can be improved by the proposed method in terms of PSNR and MSE. The proposed color image compression method has produced an average PSNR gain of 69.874 % compared with the standard JPEG color image compression method.