It was shown in \cite{GL} that the maximal surface area of a convex set in $\mathbb{R}^n$ with respect to a rotation invariant log-concave probability measure $��$ is of order $\frac{\sqrt{n}}{\sqrt[4]{Var|X|} \sqrt{\mathbb{E}|X|}}$, where $X$ is a random vector in $\mathbb{R}^n$ distributed with respect to $��$. In the present paper we discuss surface area of convex polytopes $P_K$ with $K$ facets. We find tight bounds on the maximal surface area of $P_K$ in terms of $K$. We show that $��(\partial P_K)\lesssim \frac{\sqrt{n}}{\mathbb{E}|X|}\cdot\sqrt{\log K}\cdot\log n$ for all $K$. This bound is better then the general bound for all $K\in [2,e^{\frac{c}{\sqrt{Var|X|}}}]$. Moreover, for all $K$ in that range the bound is exact up to a factor of $\log n$: for each $K\in [2,e^{\frac{c}{\sqrt{Var|X|}}}]$ there exists a polytope $P_K$ with at most $K$ facets such that $��(\partial P_K)\gtrsim \frac{\sqrt{n}}{\mathbb{E}|X|}\sqrt{\log K}.$ %For the measures $��_p$ with densities $C_{n,p} e^{-\frac{|y|^p}{p}}$ (where $p>0$) we obtain: $��_p(\partial P_K)\lesssim \frac{\sqrt{n}}{\mathbb{E}X}\sqrt{\log K},$ which was obtained for the standard Gaussian measure $��_2$ by F. Nazarov.