What's the best way to calculate an index score based on reaction time, variance and accuracy? Say I have a go/no-go task and my output data includes 3 parameters: 
average reaction time, variance of reaction time, and number of errors.
I want to composite all of the parameters into a single index score. 

What is the best way to do it? All of the parameters are equally important to me.
This overlaps a lot with these two questions: So check them out


How to measure group differences incorporating reaction time / accuracy trade-off?
How to analyze reaction times and accuracy together?


Regarding some specifics of your question, reaction time variance will often be strongly correlated with reaction time mean. So in routine measures of individual differences where speed is the main focus, it may be sufficient to focus on mean reaction time and accuracy. E.g., mean reaction time possibly with some sort of penalty for errors (see the above answers).

If you are wanting to get very precise, then there are a range of modelling approaches that may be relevant (e.g., this question on LBA and diffusion models).

With regards to go-no-go, I believe there are fairly standardised procedures for scoring such tests.