With the continued growth in Air Traffic, researchers are investigating innovative ways to increase airspace capacity while maintaining safety. A key safety indicator for an airspace is its Collision Risk estimate, which is compared against a Target Level of Safety (TLS) to provide a quantitative basis for judging the safety of operations in an airspace. However this quantitative value does not give an insight into the overall collision risk picture for an airspace, and how the risk changes given the interaction of a multitude of factors such as sector/traffic characteristics and controllers actions for flow management. In this paper, we propose an evolutionary framework with multi-objective optimization to evolve collision risk of air traffic scenarios. We attempt to identify, through evolutionary mechanism, the flight events resulting from Air Traffic Controller's actions that can lead to higher collision risks, thereby identifying the contributing factors or the events leading to collision risk. Computational experiments were conducted in an hi-fidelity air traffic simulation environment with collision risk model. Results indicate that “risk-free” traffic scenarios having collision risk below TLS can become “risk-prone” by few flight events, with Climb and Turn maneuvers, specifically during entering and exiting a sector, contributing significantly to increased collision risk.