The content ranking problem in a social news website is typically a function that maximizes a scalar metric like dwell-time. However, in most real-world applications we are interested in more than one metric— for instance, simultaneously maximizing click-through rate, monetization metrics, and dwell-time— while also satisfying the constraints from traffic requirements promised to different publishers. The solution needs to be an online algorithm since the data arrives serially. Additionally, the objective function and the constraints can dynamically change. In this project, we formulated this problem as a constrained, dynamic, multi-objective optimization problem. We proposed a novel framework that extends a successful genetic optimization algorithm, NSGA-II, to solve the ranking problem. We evaluated the optimization performance using the Hypervolume metric. We demonstrated the application of our framework on a real-world article ranking problem from the Yahoo! News page. We observed that our proposed solution makes considerable improvements in both time and performance over a brute-force baseline technique that is currently in production.