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Event argument extraction (EAE) aims to identify the arguments of a given event, and classify the roles that those arguments play. Due to high data demands of training EAE models, zero-shot cross-lingual EAE has attracted increasing attention, as it greatly reduces human annotation effort. Some prior works indicate that generation-based methods have achieved promising performance for monolingual EAE. However, when applying existing generation-based methods to zero-shot cross-lingual EAE, we find two critical challenges, including Language Discrepancy and Template Construction. In this paper, we propose a novel method termed as Language-oriented Prefix-tuning Network (LAPIN) to address the above challenges. Specifically, we devise a Language-oriented Prefix Generator module to handle the discrepancies between source and target languages. Moreover, we leverage a Language-agnostic Template Constructor module to design templates that can be adapted to any language. Extensive experiments demonstrate that our proposed method achieves the best performance, outperforming the previous state-of-the-art model by 4.8% and 2.3% of the average F1-score on two multilingual EAE datasets.