Estimating the extent and speed of invasive species' spread is crucial for optimising surveys and management, but this estimation is challenging due to the scale-dependent nature of spread. For instance, when discretising occurrence data to grids to estimate the extent of invaded ranges, larger cells can decrease boundary precision, while smaller cells can increase the risk of missing invaded areas. Moreover, on shorter timescales, such as year-to-year comparisons, spread rates can exhibit lags, accelerations and slowdowns. We present a multiscale spread optimisation methodology developed for the spotted lanternfly (Lycorma delicatula), a pest impacting grapes that is spreading in the USA. We analysed a dataset of > 900,000 occurrence records detailing spread from this pest's initial detection in eastern Pennsylvania in 2014 to its invasion front in Chicago, IL, in 2023. First, we delineated invaded area using square grids with varying cell sizes and α-convex hulls with different boundary resolutions. For each method and scale, we regressed invaded range area against year using both simple linear and logistic non-linear models. Coarser spatial scales yielded faster estimated spread rates, and logistic models outperformed linear regressions, indicating a lag, acceleration and slowdown in annual spread rate. Next, to determine the spatial scale that best captured the invasion front boundary, we used cross-validation, optimising the F_β metric, which balances recall and precision. Emphasising recall generally favoured coarser spatial scales and α-convex hulls outperformed grid-based methods. Finally, L. delicatula has dispersed long distances from its primary epicentre, establishing satellite populations. We delineated each satellite population each year since its inception using optimised α-hull values and measured their year-to-year boundary expansion. Like the primary epicentre, these satellite populations showed accelerating expansion, at least until merging with the primary invasion. Overall, the expansion rate of the primary invasion has slowed since 2017, decreasing from a maximum of 21 km/year, possibly due in part to control efforts, although the exact cause remains unresolved. Considering spatial scale and temporal variation can improve the analysis of invasive spread and motivate early interventions to manage nascent epicentres before their spread accelerates.