How to identify when to use Genetic Algorithm/Programming
The key point in deciding whether or not to use genetic algorithms for a
  particular problem centers around the question:
  
  what is the space to be searched?
  
  If that space is well-understood and contains structure that can be exploited by special-purpose search techniques, the use of genetic algorithms
  is generally computationally less efficient. If the space to be searched is
  not so well understood and relatively unstructured, and if an effective GA
  representation of that space can be developed, then GAs provide a surprisingly powerful search heuristic for large, complex spaces.


(De Jong, Machine Learning, 1990 nr 5, pg. 351)

Here a key point is what is an effective GA representation.

GA initially detects biases in low-order schemas and converges on this part of the search space. Over time, biases in higher-order schemas are detected by combining information from low-order schemas (via crossover) and eventually GA converges (at least according to the building blocks hypothesis).

Situations in which lower-order schemas give misleading information to the GA
about the probable fitness of higher-order schemas are said deceptive.

Most efforts at characterizing the difficulty of problems for GA
have concentrated on deception, but there are other factors:


the presence of multiple conflicting solutions or partial solutions
the amount of information from lower-order building blocks (not
how much this information is misleading)
sampling error
the number of local optima in the landscape
...


A general relation between a given problem's structure and the expected GA
performance on that problem is a very open area of research (actually it's not even clear what is the relation between the degree of deception in a problem and the problem's difficulty for the GA).

In general GA is more suited to find good solutions quickly than to finding the absolute best solution to a problem.

Some references you could find interesting are:


What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation by Stephanie Forrest and Melanie Mitchel (Machine Learning 1993)
Fitness distance correlation as a Measure of Problem Difficulty for Genetic Algorithms by Terry Jones and Stephanie Forrest
Dealings with Problem Hardness in Genetic Algorithms by Stjepan Picek and Marin Golub (2009)