when can xgboost or catboost be better then Logistic regression? I need to improve the prediction result of an algorithm that is already programmed based on logistic regression ( for binary classification).

I tried to use XGBoost and CatBoost (with default parameters). but it takes a long time to train the model (LR takes about 1min and boost takes about 20 min).
and if I want to apply tuning parameters it could take more time for fitting parameters. 

I want to ask if there are any suggestions to apply fastly boosting methods. 
And if there are other ways to get better performance I hope to mention them, please. 

Ps: My data is about 280 000 simples and 247 (numerical) features;
XgBoost often does better than Logistic Regression. I would use CatBoost when I have a lot of categorical features or if I do not have the time for tuning hyperparameters.

You should invest time in a boosting model for sure (they will always take more time than Logistic Regression) because it is worth it. If you are impatient, try CatBoost instead of XgBoost to see the improvement in accuracy. That will definitely give you the motivation to spend more time tuning your boosted trees.