Though Chaos Engineering is a popular method to test reliability and performance assurance, available tools can only inject random or manually curated faults into a target system. Given the vast array of faults that can be injected, it is crucial to a.) intelligently pick the faults that can have tangible effects, b.) increase the test coverage, and c.) reduce the overall time needed to assess the reliability of a system under adverse conditions. To the effect, we are proposing to learn from past major outages and use genetic algorithm-based meta-heuristics to evolve complex fault injections.