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We use them because they are particularly well suited for multi-objective optimization. They are also a global techniqe as opposed to gradient based methods and have great scaling properties. Important because each evaluation of the objective function takes ten minutes for us.

GAs have found a niche in a lot of "obscure" engineering situations. Anything where you need to optimize an expensive black box function is at least worth a try with a GA. I know they are used in analog IC design, civil engineer, and aerospace.

They can be very good when there are multiple objectives / tradeoffs. I wrote my thesis on that, which includes an introduction to the concept and how it works if you're interested. http://master.matsemann.com/

Haven't worked on them a lot recently, but AFAIK, the population based nature helps them escape local minima and do a better job at exploring the solution space.