Despite that the reference point based preference articulation plays a vital role in evolutionary multi- and many-objective optimization, three issues remain challenging. First, the performance of reference point based preference articulation largely depends on the location of the reference point. Second, the parameter settings for controlling the region of interest are not robust to the Pareto optimal fronts with different complicated shapes. Third, most existing methods have poor scalability to the number of objectives. To meet these challenges, we propose to reformulate preferences into constraints for evolutionary multi- and many-objective optimization. Extensive experiments on a variety of benchmark problems are conducted to demonstrate the effectiveness of our proposed method.