Setting best SVM hyper parameters
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)

You can use various techniques for that, for example:


Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.


That can help you find an average solution fast, but remember that this comes without warranty.


Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms


Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.

Note: If you're only going to try RBF I would advise to use $\sigma$ as a value betwen the norm of your training samples $\pm 80\%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $\sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)