<p>This work explores the use of Quantized State Systems (QSS) methods for the simulation of Stochastic Differential Equations (SDEs). To that purpose, an extension of these algorithms is proposed wherein the governing Wiener process is sampled at regular intervals, while the states are updated asynchronously when they satisfy the threshold conditions corresponding to the respective QSS method. We show that the resulting schemes produce trajectories that converge to the actual solutions of the SDEs as the sampling interval <i>h</i> and the quantum <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Delta {\textbf{Q}}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mi mathvariant="bold">Q</mi> </mrow> </math></EquationSource> </InlineEquation> approach zero. Moreover, we prove that, in stable linear time-invariant cases, the expected norm of the error is globally bounded by a linear function of the quantum <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\Delta {\textbf{Q}}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mi mathvariant="bold">Q</mi> </mrow> </math></EquationSource> </InlineEquation> and the sampling interval <i>h</i>, demonstrating that the proposed scheme preserves practical stability regardless of the choice of these parameters. We also present simulation experiments that illustrate some potential advantages of the scheme.</p>