What is the relationship between frequency vector and the peak frequency in FFT?
Although I think Jason's answer contains all the information you requested, maybe what you're looking for is a little less theoretical.

Let's assume you have a signal of $$x(t)=cos(2\pi t f_x)$$
where $t$ is the time in seconds and its frequency $f_x=20$Hz. Let's assume further you sample this signal at $f_s=1000$Hz over $T=3$ seconds, i.e. you have $K=3000$ samples $x[k]$, where $k$ is the integer sample index of the signal $x(t)$.

The sampling theorem basically says that your sampling frequency $f_s$ must be at least twice the maximum signal frequency (which is the Nyquist frequency $f_s/2$) in order to reproduce it without aliasing. For our example: $f_s=1000&gt;2f_x=40$.

You choose the "number of points" $N$ for the FFT (you called it NFFT) yourself, depending on how many different frequency samples you need to distinguish. The maximum frequency resolution you get by using all available samples you have for calculating the FFT, which - depending on the $f_s$ and $T$ of your signal - might not be feasible.

For our example, using all 3000 samples $x[k]$ is not a problem on a PC running Matlab, so let's assume we do that get $N=3000$ complex frequency samples $X[n]$ where $n$ is the integer frequency index. 

Now to your question where in $X[n]$ to find a specific frequency $f$ you're interested in if $N=K$: $$n = f T$$

Therefore, the Nyquist frequency $f_s/2$ bin is at $n_s = T f_s/2 = 1500$, right in the middle of the FFT result vector. If you plot the amplitude spectrum $abs(X)/N$, you'll notice that there are peaks at $n_x = T f_x = 60$ and $n_x' = 2940 = N - 60$, each of magnitude $1/2$. This is because the spectrum is "mirrored" around $f_s$ and the signal amplitude of 1 is divided between both peaks $n_x$ and $n_x'$.

For Matlab indices, I think you'll have to add 1 to the $n$ mentioned above because Matlab's vector indices start at 1 instead of 0.

Note that if you have frequencies $f$ which are not a multiple of your sampling frequency $f_s$, the amplitude will spread to neighbouring bins and you'll have to round $n$ to the nearest integer.

See also Matlab's FFT documentation, which has a nice example.