Pareto multiobjective genetic algorithm is one kind of vector optimization methods derived from concept of Pareto optimal

and the whole Pareto optimal set can be got using this method.Because conventional Pareto GA runs with two chromosomes crossover

it consumes much time on Niche which makes the efficency of this algorithm somewhat low.In this paper

a new Pareto multiobjective GA with multiple-chromosomes crossover is presented

and the individual is expressed with real-valued representations which makes it much faster than conventional algorithms.Based on the proof of corresponding schema theorem

variance and entropy are proposed as measurements of diversity of population in genetic algorithms.The influence that the Pareto MOGA with multiple-chromosomes crossover acts upon variance and entropy is analyzed.At last

one example is presented to compare between method in this paper and traditional methods so as to prove the superiority of this method.