  In this paper, we consider a numerical approximation of the stochastic
differential equation (SDE) $$X_{t}=x_{0}+ \int_{0}^{t} b(s, X_{s}) \mathrm{d}s
+ L_{t},~x_{0} \in \mathbb{R}^{d},~t \in [0,T],$$ where the drift coefficient
$b:[0,T] \times \mathbb{R}^d \to \mathbb{R}^d$ is H\"older continuous in both
time and space variables and the noise $L=(L_t)_{0 \leq t \leq T}$ is a
$d$-dimensional L\'evy process. We provide the rate of convergence for the
Euler-Maruyama approximation when $L$ is a Wiener process or a truncated
symmetric $\alpha$-stable process with $\alpha \in (1,2)$. Our technique is
based on the regularity of the solution to the associated Kolmogorov equation.
