In evolution strategies, the individuals are coded as vectors of real numbers. On reproduction, parents are selected randomly and the fittest offsprings are selected and inserted in the next generation. ES individuals are self-adapting. The step size or “mutation strength” is encoded in the individual, so good parameters get to the next generation by selecting good individuals.
In genetic algorithms, the individuals are coded as integers. The selection is done by selecting parents proportional to their fitness. So individuals must be evaluated before the first selection is done. Genetic operators work on the bit-level (e.g. cutting a bit string into multiple pieces and interchange them with the pieces of the other parent or switching single bits).
That’s the theory. In practice, it is sometimes hard to distinguish between both evolutionary algorithms, and you need to create hybrid algorithms (e.g. integer (bit-string) individuals that encodes the parameters of the genetic operators).