With the exponential growth of mobile devices, Mobile Crowdsensing (MCS) has emerged as a new paradigm for various types of tasks. However, most existing studies focus primarily on the utility of either buyers or sellers, with limited exploration of the task types for buyers and the user types for sellers. Therefore, this paper investigates the incentive mechanisms for different types of sellers under varying task types. In this study, we classify sellers into two groups: teams and individuals, and classify buyers' tasks into two categories: simple tasks and complex tasks. Considering the characteristics of different task types and seller groups, we design two auction schemes: the Two-Stage Auction Scheme for Simple Tasks (TDAS-S) and the Two-Stage Auction Scheme for Complex Tasks (TDAS-C). In the first stage, we design a seller auction model based on the Stackelberg game to ensure the maximization of the seller's utility. In the second stage, we design an auction model for buyers, utilizing a variant of the Vickrey Auction and marginal contribution theory to pay the selected sellers in both TDAS-S and TDAS-C, ensuring the maximization of the buyer's utility. We further prove that the proposed scheme satisfies properties such as individual rationality, truthfulness, and budget balance. Finally, through extensive experiments on real-world datasets, we demonstrate that our scheme effectively balances the utility conflicts between buyers and sellers, allowing each party to maximize their respective utilities.