The rapid development of crowdsourcing has enriched workers’ experience and skills of workers， making them more aware of tasks and tend to complete multiple tasks at the same time. Therefore， assigning tasks according to workers’ subjective preferences has become a common way of task assignment. However， out of personal interests， workers may take malicious bidding behaviors to obtain higher utility. It is detrimental to the development of crowdsourcing platforms. To this end， an incentive mechanism of crowdsourcing multi-task assignment against malicious bidding was proposed， named GIMSM （Greedy Incentive Mechanism for Single-Minded）. First， a linear ratio was defined as the allocation basis by this mechanism. Then， according to the greedy strategy， from a sequence of increasing worker ratios， tasks were selected and assigned. Finally， the workers selected by allocation algorithm were paid according to payment function， and the result of task assignment was obtained. The experiments were conducted on Taxi and Limousine Commission Trip Record Data dataset. Compared to TODA （Truthful Online Double Auction mechanism）， TCAM （Truthful Combinatorial Auction Mechanism） and FU method， GIMSM’s average quality level of task results under different numbers of workers increased by 25.20 percentage points， 13.20 percentage points and 4.40 percentage points， respectively. GIMSM’s average quality level of task results under different numbers of tasks increased by 26.17 percentage points， 16.17 percentage points and 9.67 percentage points， respectively. In addition， the proposed mechanism GIMSM satisfies individual rationality and incentive compatibility， and can obtain task assignment results in linear time. The experimental results show that the proposed mechanism GIMSM has good anti-malicious bidding performance， and has a better performance on the crowdsourcing platforms with a large amount of data.