This paper proposes an multi-objective evolutionary algorithm. The algorithm is based on OMOEA-II. A new linear breeding operator with lower-dimensional crossover and copy operation is used. By using the lower-dimensional crossover, the complexity of searching is decreased so the algorithm converges faster. The orthogonal crossover increase probability of producing potential superior solutions, which helps the algorithm get better results. Ten unconstrained problems are used to test the algorithm. For three problems, the obtained solutions are very close to the true Pareto front, and for one problem, the obtained solutions distribute on part of the true Pareto front.