With the development of mobile services’ computing and sensing abilities, spatial-temporal crowdsourcing, which is based on location information, comes into being. There are many challenges to improving the performance of task assignments, one of which is how to assign workers the tasks that they are interested in. Existing research methods only focus on workers’ temporal preference but ignore the impact of spatial factors on workers’ preference, and they only focus on long-term preference but ignore workers’ short-term preference and face the problem of inaccurate predictions caused by sparse historical data. This study analyzes the task assignment problem based on long-term and short-term spatio-temporal preference. By comprehensively considering workers’ preferences from both long-term and short-term perspectives, as well as temporal and spatial dimensions, the quality of task assignment is improved in task assignment success rate and completion efficiency. In order to improve the accuracy of spatio-temporal preference prediction, the study proposes a sliced imputation-based context-aware tensor decomposition algorithm (SICTD) to reduce the proportion of missing values in preference tensors and calculates short-term spatio-temporal preference through the ST-HITS algorithm and short-term active range of workers under spatio-temporal constraints. In order to maximize the total task reward and the workers’ average preference for completing tasks, the study designs a spatio-temporal preference-aware greedy and Kuhn-Munkres (KM) algorithm to optimize the results of task assignment. Extensive experimental results on real datasets show the effectiveness of the long- and short-term spatio-temporal preference-aware task assignment framework. Compared with baselines, the RMSE prediction error of the proposed SICTD for temporal and spatial preferences is decreased by 22.55% and 24.17%, respectively. In terms of task assignment, the proposed preference-aware KM algorithm significantly outperforms the baseline algorithms, with the workers’ total reward and average preference for completing tasks averagely increased by 40.86% and 22.40%, respectively.