Abstract: A Sine-Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. Sine-Cosine Algorithm (SCA) is inspired from the sine and cosine mathematical functions. The standard Sine-Cosine Algorithm (SCA) has some problems, like any of the other techniques such as slow convergence and falling into local solutions. To overcome these problems, this study suggested four different chaotic Sine-Cosine Algorithms (CSCAs) methods. The random parameters in the standard Sine-Cosine Algorithm (SCA) are replaced with the chaotic sequences to improve the performance of the standard algorithm. Five one dimensional various chaotic maps are implemented.The proposed chaotic Sine-Cosine Algorithms (CSCAs) methods are benchmarked on ten test benchmark functions. The statistical results showed that all chaotic Sine-Cosine Algorithms (CSCAs) methods can be outperformed the standard Sine-Cosine Algorithm (SCA) for these benchmark functions and the intermittency and circle maps are the best maps for boosting the performance of the first and fourth chaotic CSCAs. While the Gauss map is the most suitable variant for the second and third chaotic CSCAs methods, respectively. Additionally, the results proved that the fourth proposed algorithm with the circle map significantly overtook on the other proposed algorithms. The effectiveness of all chaotic Sine-Cosine Algorithms (CSCAs) methods are proved by comparing their results with the well-known metaheuristic methods such as the standard Sine-Cosine Algorithm (SCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE).