Distributed tracing is a pivotal technique for software operators to understand and diagnose issues within microservice-based systems, offering a comprehensive view of user requests propagated through various services. However, the unprecedented volume of traces imposes expensive storage and analytical burdens for online systems. Conventional tracing implementations typically use random sampling with a fixed probability for each trace, posing a risk of losing valuable traces. To circumvent this loss, several tail-based sampling methods have been proposed to sample traces based on their content. Nevertheless, these methods primarily evaluate traces on an individual basis, neglecting the collective attributes of the sample set, including its comprehensiveness, balance, and consistency. To address these issues, we propose TracePicker, an optimization-based online sampler designed to enhance the quality of sampled data while mitigating storage burden. TracePicker employs a streaming anomaly detector to capture and retain anomalous traces that are crucial for troubleshooting. For normal traces, the sampling process is segmented into quota allocation and group sampling, formulating both as optimization problems. By solving these problems using dynamic programming and evolution algorithms, TracePicker selects a high-quality subset of data, minimizing overall information loss. Experimental results demonstrate that TracePicker outperforms existing tail-based sampling methods in terms of both sampling quality and time consumption.