The simulated binary crossover (SBX) has been extensively adopted in the real-coded multiobjective evolutionary algorithms (MOEAs). Through the comparisons and analyses of the SBX and the mutation operator in the evolution strategy (ES), this paper proposes a normal distribution crossover (NDX) with the introduction of discrete recombination operator in ES. The NDX and SBX operators are compared and analyzed through an example designed in the one dimensional search space, and then the NDX is applied to a steady-state multiobjective evolutionary algorithm named ε-MOEA (ε-dominance based multiobjective evolutionary algorithm) proposed by Deb, et al. The algorithmε-MOEA with NDX (ε-MOEA/NDX) has been tested and compared on the 10 benchmark functions taken from the ZDT and DTLZ standard test suites. Experimental results demonstrate that algorithmε-MOEA/NDX is distinctly superior to theε-MOEA/SBX and NSGA-II algorithms, which are representatives of the state-of-the-art in the area.