With the rapid development of large language model (LLM) technology, many Code LLMs have emerged to support tasks such as code generation, code completion, code testing, and code refactoring. Different models may show significant performance differences when processing the same task, and the decoding parameters at the inference stage will also have an important influence on model performance. This study investigates how to efficiently select the best model and its optimal decoding parameters for a specific code development task. Existing methods generally divide model selection and parameter tuning into two independent stages. Due to the differences in sampling strategies at different stages, sample data cannot be shared, and the computational cost of sampling and evaluation is high. Considering that the decoding parameter space of different Code LLMs is the same, this study proposes the utilization of the propensity score matching (PSM) algorithm for conducting weighted adjustment and aligning sample data of different distributions to improve the reuse efficiency of sample data and reduce computational costs. Therefore, this study proposes a framework CodeLLMTuner for Code LLM selection and decoding parameter tuning based on sample reuse. The framework includes three stages, including the independent sampling stage, which performs decoding parameter tuning (such as Bayesian optimization) on multiple Code LLMs in parallel and conducts data sampling and evaluation to collect sample data. Additionally, the model selection stage adopts PSM technology to align the sample data of different models and selects the model with the optimal performance expectations. The decoding parameter tuning stage of the selected model reuses the sample data of the selected model and continues decoding parameter tuning based on it to fully explore the performance space and significantly reduce sampling costs. Experimental results show that in the three tasks of code generation, code summarization and test case generation, CodeLLMTuner improves performance by 10% to 15% at the same cost compared to the baseline methods, or reduces the cost by more than 20% under the same performance.