In crowdsourcing platforms, orders have different types (takeaway and express orders), while delivery riders are typically responsible for only one type of order (either takeaway or express delivery). Additionally, the existing delivery mechanism rarely meets the satisfaction of merchants and customers. Therefore, considering the heterogeneity of riders in a dispatch mode, this study introduces the concept of all-round riders, dividing riders into three categories: takeaway riders express riders, and all-round riders. According to the differences in the types of orders that riders can serve, a cost function based on a fuzzy time window is constructed to represent the satisfaction of merchants and customers with the time when riders arrive at pick-up and delivery points. The satisfaction is then transformed into a time penalty function. A model is constructed to minimize time penalty costs, route driving costs and personnel operation costs. Considering the characteristics of the model and the limitations of traditional algorithms, this study designs a hybrid algorithm combining genetic algorithms and search algorithms in large domains. Then, the simulated annealing algorithm, genetic algorithms, and hybrid algorithm are used to solve the problem respectively through concrete examples. The analysis of the optimization results of different algorithms validates the feasibility and effectiveness of the proposed model and the improved algorithm. Experimental results show that considering the heterogeneity of riders and the satisfaction of merchants and customers during crowdsourcing delivery not only effectively improves their satisfaction but also reduces delivery costs and improves delivery efficiency for crowdsourcing platforms. This strategy offers a reference for crowdsourcing platforms in formulating delivery strategies.