Because most antennas don't have weird enough requirements so standard designs work fine. For stuff down here on Earth we deal with a lot of the same problem so the problem has generally been solved to a reasonable approximation and there's additional constraints like shape and space. Standard designs are also just cheaper because they're made by the thousands and machines exist to make them in massive quantities.
Talking less about antennas but more about the overall concept, most evolutionary algorithms don't take manufactoring into account at all, so they tend to be a pain to actually build.
Because that would be unnecessary. This is only worth it when there are "unusual radiation patterns" (from the wiki article)
I do a lot of antenna design. There are far too many variables for optimization, and also size/geometry constraints. That's not to say you wouldn't start with a base design and use an optimizer (which could be genetic). There was a company commercializing this exact algorithm several year ago. I don't think it went anywhere.
Genetic optimisers don't tend to do well over a few hundred parameters, but stochastic gradient descent does well up to billions of parameters as long as you have sufficient compute and ram. Obviously your fitness function needs to be differentiable with respect to the parameters, which most simulators can't yet do.