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Indexing: One of the most straightforward ways to decrease the execution time of a MongoDB query is by indexing the fields that are frequently accessed. Indexing helps by creating a sorted data structure that makes searching faster.

Proper Query Optimization: Optimizing queries is vital in minimizing query execution time. The use of the right query operators, such as $gt, $gte, $lt, and $lte, can help in reducing the documents returned and, ultimately, the query time.

Proper Query Design: Proper query design is also essential in decreasing the execution time of a MongoDB query. Queries that are more compact and efficient are much faster and run smoother.

Sharding: When dealing with massive data sets, sharding can scale horizontally and distribute resources across different servers, making data access faster.

Replication: By having multiple copies of the data in different geographical locations, data access times are significantly reduced since the data is stored closer to the user.

Avoid Over-fetching & Under-fetching: Over-fetching means returning more data than necessary, leading to slower query execution time, whereas under-fetching means returning less data and then performing another query to retrieve more information. To avoid this, it is essential to know what data is required and return only that data.

Using Aggregation Pipeline: MongoDB's Aggregation Pipeline is an efficient and powerful tool that consolidates data processing and reduces query execution time.

Caching: Caching frequently accessed data in memory can significantly reduce query execution time. It saves time by reducing the number of times these queries hit the storage layer since it fetches the data from memory.

Considering Data Structure: MongoDB's document-based storage structure also plays a significant role in query execution time. By structuring data in a way that makes it easy to find and process, query execution times can be decreased.