Optimizing recurrent neural networks involves tuning learning rates with Adam or schedules, applying gradient clipping to prevent exploding gradients, using L2 regularization and dropout to reduce overfitting, experimenting with LSTM or GRU layers, and incorporating batch normalization for stable training and improved performance.