Contrast enhancement techniques that are proposed in the literature are devised to enhance image quality so as to provide better details for different image processing tasks. Histogram Equalization (HE) is a widely exploited technique to enhance contrast of the images. Histograms are used to measure the frequency of intensity levels in an image. The objective of HE is to distribute the intensity values in an image such that the lower contrast areas can gain higher contrast. HE techniques can be global or local. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a local HE method. Thus, CLAHE can handle the problems of the classical HE algorithm. CLAHE employs two parameters, which are, respectively, number of tiles and clip limit. The efficiency of the CLAHE depends on these two parameters. Hence, the results can be changed if the parameters of the CLAHE is adjusted. The value of the optimal parameters can vary according to the image type. In this paper, Multi-objective Cuckoo Search (MOCS) is employed to determine optimal parameters for CLAHE to enhance the contrast of images. For MOCS, first fitness function is set for computing Entropy and second fitness function is set for computing Fast Noise Variance Estimation (FNVE) so as to provide a good local detail preservation and prevent noise amplification in the output image. Image dataset is taken from Contrast Enhancement Evaluation Database (CEED2016), which can be found in Mendeley data repository. This database is used for evaluating the overall performance of the proposed method. Evaluation results of the proposed method are given in terms of Absolute Mean Brightness Error (AMBE), Peak Signal-to-noise Ratio (PSNR), Mean Squared Error (MSE), Maximum Difference (MD), Mean Absolute Error (MAE) and Computing Time (CT) .