This study proposes an optimized Multi-Histogram Equalization (OMHE) technique for contrast enhancement while preserving the brightness of an input image. The objective of this study is to improve the visual interpretability or perception of information among color images.  In this technique, input image histogram is partitioned into multiple sub-histograms and then classical histogram equalization process is applied to each one. Values of t threshold points for dividing the image histogram into t+1 sub-histograms are optimized using Artificial Bee Colony, a swarm intelligence-based optimization algorithm. A new fitness function for evaluating the contrast of enhanced image is proposed here that will guide the Artificial Bee colony algorithm into finding the optimal threshold values. AMBE (Absolute Mean Brightness Error), PSNR (Peak signal to noise ratio), SSIM (Structural Similarity Index) and Entropy are computed for quantitative analysis of the performance of the proposed method with existing methods. Comparisons show that proposed method performs better than other present approaches by enhancing the contrast well while preserving the brightness of the input image.