Simultaneous childhood epilepsy syndrome classification (ESC) and seizure detection (SD) are both significant in epilepsy analysis. Current research mainly focuses on a single task, mostly on SD. In this article, a novel dual-stream multitask network (DSMN) exploiting multichannel scalp electroencephalograms (EEGs) is developed to simultaneously perform ESC-Task and SD-Task, in short as DSMN-ESS. The close correlation between ESC-Task and SD-Task is explored to achieve better performance. To improve the performance, an information-sharing gate module is designed in DSMN to enable both tasks to fully obtain useful information. Meanwhile, a channel weight update module is developed to well-extract the internal spatial relationship between multichannel EEGs. Furthermore, an area-under-the-curve (AUC)-based loss is proposed to address the data imbalance issue in epilepsy analysis. Studies on EEG data recorded 49 patients from the Children’s Hospital, Zhejiang University School of Medicine (CHZU), are carried out to show the effectiveness of DSMN-ESS. The results show that DSMN-ESS can achieve the highest AUC, 99.95% and 99.78% in ESC-Task and SD-Task, respectively, which are superior to several state-of-the-art (SOTA) methods.