The optimization of Latin hypercube design (LHD) is an important area of the space filling experimentation. There are procedures to find good LHD by minimizing the pairwise correlations or maximizing the inter-site distance. we have shown that these two criteria need not agree with each other, that is, minimizing of pairwise correlations can result in LHDs where the inter-site distances is high and vice versa. Therefore, a multi-objective optimization approach to find good LHD is proposed by combining correlation and distance performance measures with modified ESE algorithm. Several examples are presented to show that the new algorithm is fast and the optimal designs are good in terms of both the correlation and distance criteria.