This paper considers robust estimation of moment condition models with time series data. Researchers frequently use moment condition models in dynamic econometric analysis. These models are particularly useful when one wishes to avoid fully parameterizing the dynamics in the data. It is nevertheless desirable to use an estimation method that is robust against deviations from the model assumptions. For example, measurement errors can contaminate observations and thereby lead to such deviations. This is an important issue for time series data: in addition to conventional sources of mismeasurement, it is known that an inappropriate treatment of seasonality can cause serially correlated measurement errors. Efficiency is also a critical issue since time series sample sizes are often limited. This paper addresses these problems. Our estimator has three features: (i) it achieves an asymptotic optimal robust property, (ii) it treats time series dependence nonparametrically by a data blocking technique, and (iii) it is asymptotically as efficient as the optimally weighted GMM if indeed the model assumptions hold. A small scale simulation experiment suggests that our estimator performs favorably compared to other estimators including GMM, thereby supporting our theoretical findings.