Personally I use them all the time, regardless of variable type, typically for low-dimensional (<= 100 variables) black-box optimization problems of unknown structure where I want an approximate solution quickly.
For example, this solver uses a GA and accepts continuous and discrete variables:
In the continuous smooth world, if a method doesn't use derivatives, its effectiveness tends to decay like one divided by the square-root of the dimension for the same work per iteration; so I might be more focused on the number of variables as opposed to variable type. (Look at equations 3.10 define K(G) here (DOI link), though not used in GA's I believe they work probabilistically the same way. The dimension explodes and you cannot possibly keep pace. At best you have an underlying spanning set whose effectiveness degrades rapidly with dimension.)
However, to avoid being rudely laughed at, if you are publishing results,
a basic rule of thumb is to apply the solver in your toolbox that matches your optimization problem closest. For example, if you are solving a TSP and have SAS/OPTMILP, don't use a GA. GA's assume almost nothing about the problem while a MILP solver is very specific on problem type. The more assumptions a solver makes about the problem and its structure, the more targeted/expeditious the algorithm design becomes.
Consider group theory (https://www.britannica.com/science/group-theory). It arises from 4 simple assumptions a child might understand (axioms) that result in volumes of complex implications and hours upon hours of mathematical fun. Never under estimate the power limiting assumptions in the hands of a motivated mathematician/developer:)
Of course, if you were on a desert island and had to code up a solver from scratch to find the best solution that day to stay on the island, metaheuristics start to look a lot more attractive, regardless of problem type. I.e. always take your pre-solve set-up time into account. If you spend 3 weeks setting things up to reduce the solve time from 3 hours to 3 minutes, not a clear win ... unless it is for a paper:)