Genetic Algorithms (GAs) are a sub-class of Evolutionary Algorithms (EAs). The salient feature of GAs is that they replicate evolution by invoking a close, low-level metaphor of biological genes. If this seems obvious or redundant, remember that the theory of evolution was formulated independently of genetics!

From AI - A Modern Approach (Russel & Norvig):

Evolutionary algorithms ... are explicitly motivated by the metaphor of natural selections in biology: there is a population of individuals (states), in which the fittest (highest value) individuals produce offspring (successor states) that populate the next generation, a process called recombination.

Then later:

In genetic algorithms, each individual is a string over a finite alphabet, just as DNA is a string over the alphabet ACGT.

I certainly wasn't aware of this distinction before reading it in R&N. It makes sense to me, although I know that even experts in GAs refer to the use of continuous-value implementations of EAs such as NSGAII as 'genetic'. So I guess it's mostly a technicality.