Genetic Algorithm in Optimization of Events
I'm unsure of what your question is, but here are the elements you need for any GA:
A population of initial "genomes"
A ranking function
Some form of mutation, crossing over within the genome
and reproduction.
If a critical event is always the same, your GA should work very well.  That being said, if you have a different critical event but the same genome you will run into trouble.  GA's evolve functions towards the best possible solution for A Set of conditions.  If you constantly run the GA so that it may adapt to each unique situation you will find a greater degree of adaptability, but have a speed issue.
You have a distinct advantage using python because string manipulation (what you'll probably use for the genome) is easy, however...
python is slow.
If the genome is short, the initial population is small, and there are very few generations this shouldn't be a problem. You lose possibly better solutions that way but it will be significantly faster.
have fun...
I'm  a data analysis student and I'm starting to explore Genetic Algorithms at the moment. I'm trying to solve a problem with GA but I'm not sure about the formulation of the problem. 
Basically I have a state of a variable being 0 or 1 (0 it's in the normal range of values, 1 is in a critical state). When the state is 1 I can apply 3 solutions (let's consider Solution A, B and C) and for each solution I know the time that the solution was applied and the time where the state of the variable goes to 0. 
So I have for the problem a set of data that have a critical event at 1, the solution applied and the time interval (in minutes) from the critical event to the application of the solution, and the time interval (in minutes) from the application of the solution until the event goes to 0. 
I want with a genetic algorithm to know which is the best solution for a critical event and the fastest one. And if it is possible to rank the solutions acquired so if in the future on solution can't be applied I can always apply the second best for example.
I'm thinking of developing the solution in Python since I'm new to GA.
Edit: Specifying the problem (responding to AMack)
Yes is more a less that but with some nuances. For example the function A can be more suitable to make the variable go to F but because exist other problems with the variable are applied more than one solution. So on the data that i receive for an event of V, sometimes can be applied 3 ou 4 functions but only 1 or 2 of them are specialized for the problem that i want to analyze. My objetive is to make a decision support on the solution to use when determined problem appear. But the optimal solution can be more that one because for some event function A acts very fast but in other case of the same event function A don't produce a fast response and function C is better in that case. So in the end i pretend a solution where is indicated what are the best solutions to the problem but not only the fastest because the fastest in the majority of the cases sometimes is not the fastest in the same issue but with a different background.