A new evolutionary computing algorithm on the basis of "jumping genes" phenomenon is presented in this article. It emulates the gene transposition in the genome that was discovered by N obel Laureate D r. Barbara M cClintock from her work on maize chromosome. T he principle of jumping genes, adopted for evolutionary computing, is outlined and the procedures for executing the computational optimization are provided. M athematical derivation of the S chema Theorem is briefly discussed, which is established to demonstrate the searching capacity of the newly proposed algorithm, in terms of convergence and diversity. T he algorithm is found to be robust and provides outcomes in speed and accuracy, while the solutions are widely spread along the Pareto-optimal front when a multiobjective problem is tackled. T o further reinforce the jumping genes proposition, some typical engineering design problems are included. T he obtained results have indicated that this new algorithm is indeed capable of searching multiobjective solutions including the extreme solutions at both ends of the Pareto-optimal front. © 2006 IEEE.