I strongly disagree, though it depends on your definition of what machine learning really is.
If you define machine learning algorithms as those that learn from data, then okay. In that case, EAs are reinforcement learning algorithms and other methods like Q-learning are also not ML algorithms.
However, I use the canonical definition by Mitchell: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. In this case, it's clear that in fact EAs are very much so an ML method. One could even go so far as to say they are more appropriately ML than things like SVMs as they are truly learning from experience rather than just data being handed to them.
I don't necessarily disagree with you. However, I find that most contemporary real-world ML applications (from face recognition to collaborative filtering to computer vision) do use algorithms that "learn from data", using some kind of learning technique, i.e. supervised, unsupervised or a combination of those.
The process of creating that kind of algorithm is also very different: it is based heavily on mathematical/statistical/probabilistic methods and hence the resulting algorithm can be proven that it works with some kind of certainty. On contrary, creating an EA is mostly some kind of "art" (as one UCL professor put it).
All in all, I can't help but feel that even though they are both approaches to the same problem ("how can a computer program learn?"), data-driven methods and EA algorithms don't share much more. And since the results produced by the former are what most people expect of an ML algorithm to accomplish, I tend to think of those when using the phrase "machine learning".
But it's all about semantics in the end.
// I noticed that you are an ML PhD student. So you know exactly what I'm talking about.