Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2009.3652.3661AdeyemoJ.A.O. OtienoF.A.122009920Many engineering problems are characterized with multi-objectives. Differential Evolution (DE), an Evolutionary Algorithm (EA), known to be fast and robust in numerical optimization is extended to multi-objective problems in this study. The new algorithm named Multi-objective Differential Evolution Algorithm (MDEA) adjusts the selection scheme of traditional DE to solve multi-objective problems. The algorithm also modifies the domination criteria for the population. The offspring generated in subsequent generations are improved before domination check is performed on the population in the final generation. Moreover, trial solution replaces the target solution if it is better or equal in all the objectives. The proposed algorithm is coded in MATLAB 7.0 and has been successfully applied to five common test problems and an engineering cantilever design problem. Good spread of quality Pareto optimal solutions are achieved. The algorithm produces more Pareto optimal solutions than the previous algorithms and retains the fast convergence and diversity exhibited by DE in global optimization. The algorithm is a good choice for solving many practical engineering problems with ease.]]>Abbass, H.A. and R. Sarker,2002Rakesh, A. and B.V. Babu,2005Babu, B.V. and M.M. Jehan,2003Babu, B.V., J.H.S. Mubeen and G.C. Pallavi,2005Deb, K.,1999Deb, K.,2001Deb, K., A. Pratap, S. Agarwal and T. Meyarivan,2002Fan, H.Y., J. Lampinen and L. Yeshayahou,2006Madavan, N.K.,2002Storn, R. and K. Price,1997Reddy, M.J. and D.N. Kumar,2007Robic, T. and B. Filipic,2005Santana-Quintero, L.V. and C.A.C. Coello,2005Xue, F., A.C. Sanderson and R.J. Graves,2003Zitzler, E., K. Deb and L.Thiele, 2000Zitzler, E. and L. Thiele,1999Price, K. and R. Storn,2008